An improved method and system for performing a sequence of neurofeedback and cognitive training in a neurofeedback training session

Abstract

A method and a system of optimizing a neurofeedback training session are presented. The neurofeedback training session includes an alternating sequence of neurofeedback training periods and cognitive tasks to be performed by a user on a computer device. An EEG signal is obtained from electrodes worn on the user's head during the neurofeedback training periods and during the cognitive tasks, the EEG signal including, amongst others, alpha waves and beta waves. A relative power is determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. One or more characteristics of the relative power over time are determined during the neurofeedback training periods and during the cognitive tasks. A duration of the neurofeedback training periods and / or the cognitive tasks are set based on the one or more characteristics.

Description

[0001] An improved method and system for performing a sequence of neurofeedback and cognitive training in a neurofeedback training session

[0002] Technical field

[0003] The present disclosure is generally related to biofeedback. In particular, the present disclosure relates to performing a sequence of neurofeedback training and cognitive training in a neurofeedback training session.

[0004] Background

[0005] Conventional approaches to biofeedback exist, wherein a bio-signal of a user is measured, a sensory signal is fed back to the user, and wherein the user actively performs relaxation or meditation techniques, in particular in order to relieve stress.

[0006] A bio-signal of a user can be measured. The user may listen to an audio signal to which a filter is applied for variably filtering the audio signal by modifying a cut-off frequency in response to the bio-signal in order to reduce human stress and enhance measurable mental performance.

[0007] Summary

[0008] A summary of aspects of certain examples disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects and / or a combination of aspects that may not be set forth.

[0009] It is known to interleave tasks or trainings, for example cognitive training tasks and neurofeedback training tasks, during a single session. This is also known as interval training. Traditionally, there has been little attention to the development of the users power spectra during these tasks or trainings. For example, it is plausible that some users gain optimum advantage from the cognitive task or neurofeedback training long before the cognitive task or neurofeedback trainings is completed. It can therefore be unclear when to terminate a cognitive task or neurofeedback training and when to transition between cognitive tasks and neurofeedback trainings for individual users.

[0010] The present disclosure enables detecting an electroencephalogram (EEG) pattern during a neurofeedback training session and during cognitive training periods and provides solutions for acting to optimize the sequence and duration of the alternating neurofeedback training periods and cognitive training periods, typically given a maximum acceptable neurofeedback training session duration.

[0011] According to an aspect of the present disclosure, a method of optimizing a neurofeedback training session is presented. The neurofeedback training session includes an alternating sequence of neurofeedback training periods and cognitive tasks to be performed by a user on a computer device. The method may include obtaining an EEG signal from electrodes worn on the user’s head during the neurofeedback training periods and during the cognitive tasks. The EEG signal includes brain waves including, amongst others, alpha waves and beta waves. The method may further include determining a relative power based on at least an alpha power of the alpha waves and a beta power of the beta waves. The method may further include determining one or more characteristics of the relative power over time during the neurofeedback training periods and during the cognitive tasks. The method may further include setting a duration of the neurofeedback training periods and / or of the cognitive tasks based on the one or more characteristics.

[0012] In an embodiment, the method may further include adapting the sequence of the neurofeedback training periods and the cognitive tasks based on the one or more characteristics.

[0013] In an embodiment, the method may further include determining a termination time for a neurofeedback training period based on the one or more characteristics.

[0014] In an embodiment, the method may further include determining a termination time for a cognitive tasks based on the one or more characteristics.

[0015] In an embodiment, the method may further include determining one or more parameters of a neurofeedback sensory signal for use during a neurofeedback training period based on the one or more characteristics.

[0016] In an embodiment, the method may include determining one or more parameters of a cognitive task based on the one or more characteristics. In an embodiment, the method may include determining a neurofeedback training period or a cognitive tasks to be performed after a current neurofeedback training period or a current cognitive task based on the one or more characteristics.

[0017] In an embodiment, the one or more characteristics may be based on a duration of a neurofeedback training period when maximizing the impact of a resilience to stress training during the neurofeedback training periods. The method may include comparing the duration of a plurality of the neurofeedback training periods. The one or more parameters may set the duration of a next neurofeedback training period.

[0018] In an embodiment, the method may include a training for resilience to stress. The method may include determining a plateau rise time in the relative power during a neurofeedback training period for training for resilience to stress. The duration of the neurofeedback training period for training for resilience to stress may be based on the determined plateau rise time.

[0019] In an embodiment, a transition time for terminating the neurofeedback training period for training for resilience to stress and transitioning to a cognitive task may be determined based on the plateau rise time and a predefined period for maintaining a plateau level in the relative power.

[0020] In an embodiment, the duration of the neurofeedback training period for training for resilience to stress may be calculated as (1 +X) x Tp, with X being the predefined period and TP being the plateau rise time.

[0021] In an embodiment, for training for resilience to stress, 0<X<1 .

[0022] In an embodiment, the method may include a training for staying in a flow. The method may include determining a plateau rise time in the relative power during a neurofeedback training period for training for staying in a flow. The duration of the neurofeedback training period for training for staying in a flow may be based on the determined plateau rise time.

[0023] In an embodiment, a transition time for terminating the neurofeedback training period for training for staying in a flow and transition to a cognitive task may be determined based on the plateau rise time and a predefined period for maintaining a plateau level in the relative power.

[0024] In an embodiment, the duration of the neurofeedback training period for training for staying in a flow is calculated as (1 +X) x Tp, with X being the predefined period and TP being the plateau rise time. In an embodiment, for training for staying in a flow, X>1 .

[0025] In an embodiment, the method may further include determining a start time in the relative power of the neurofeedback training period. The duration of the neurofeedback training period may be based on the determined start time.

[0026] In an embodiment, the method may further include determining a delta time in the relative power of the neurofeedback training period where a rise time in the relative power starts. The duration of the neurofeedback training period may be based on the determined delta time.

[0027] In an embodiment, the method may further include determining a fall time in the relative power where the relative power ratio starts to drop. The duration of the neurofeedback training period may be based on the determined fall time.

[0028] In an embodiment, the method may include a training for resilience to stress. The method may include determining a plateau rise time in a beta power based relative power during a cognitive task for training for resilience to stress. The duration of the cognitive task for training for resilience to stress may be based on the determined plateau rise time.

[0029] In an embodiment, a transition time for terminating the cognitive task for training for resilience to stress and transitioning to a neurofeedback training period may be determined based on the plateau rise time and a predefined period for maintaining a plateau level in the beta power based relative power.

[0030] In an embodiment, the duration of the cognitive task for training for resilience to stress may be calculated as (1 +Y) x TSP, with Y being the predefined period and TSP being the plateau rise time.

[0031] In an embodiment, for training for resilience to stress, 0<Y<1 .

[0032] In an embodiment, the method may further include determining a fall time in the relative power where the relative power ratio starts to drop. The duration of the cognitive task may be based on the determined fall time.

[0033] In an embodiment, the relative power may be determined by calculating a ratio of alpha power over beta power. Alternatively, the relative power may be determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power.

[0034] In an embodiment, the relative power may be determined by calculating a ratio of beta power over alpha power. Alternatively, the relative power may be determined by calculating a ratio of beta power over a total brainwave power or over another brain wave power.

[0035] According to an aspect of the present disclosure, a neurofeedback system is presented. The neurofeedback system may include a processing system configured for capturing an EEG signal from electrodes worn on a user’s head. The neurofeedback system may further include a neurofeedback playback device for presenting a neurofeedback sensory signal to the user during a neurofeedback training period. The processing system may include a processing module configured for adjusting one or more parameters of the neurofeedback sensory signal relative to a default setting of the neurofeedback sensory signal. The processing module may further be configured for adjusting one or more parameters of a cognitive task to be performed by a user on a computer device. The processing system may be configured to perform the method having one or more of the above described features.

[0036] In an embodiment, the neurofeedback system may further include a wearable device including an EEG sensor comprising the electrodes for capturing the EEG signal. The wearable device (2108) may be wirelessly communicatively connected to the processing system.

[0037] In an embodiment, the neurofeedback system may further include an intermediary device configured to relay the EEG signal from the wearable device to the processing system.

[0038] In an embodiment, the intermediary device may be a smartphone.

[0039] According to an aspect of the present disclosure, a computer program is presented. The computer program may include instruction which, when the program is executed by one or more processors, cause the one or more processors to carry out the method having one or more of the above-described features.

[0040] According to an aspect of the present disclosure, a computer-readable storage medium is presented. The computer-readable storage medium may include instructions which, when executed by one or more processors, cause the one or more processors to carry out the method having one or more of the above-described features.

[0041] Brief description of the Drawings Embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying schematic drawings in which corresponding reference symbol indicate corresponding parts, in which:

[0042] Fig. 1 shows a top view of a human head indicating positions for placing electrodes;

[0043] Fig. 2 shows an example of a neurofeedback training session, wherein various tasks are performed;

[0044] Fig. 3 shows an example of a neurofeedback training program including a plurality of neurofeedback training sessions;

[0045] Fig. 4 shows an example EEG pattern, in the form of a relative power a / p, obtained during a neurofeedback training period;

[0046] Fig. 5 shows an example EEG pattern, in the form of a relative power a / p, obtained during a neurofeedback training session;

[0047] Fig. 6 shows an example EEG pattern, in the form of a relative power a / p, of a rise period during a neurofeedback training period;

[0048] Fig. 7 shows an example EEG pattern, in the form of a relative power a / p, during a neurofeedback training period;

[0049] Fig. 8 shows example graphs of cognitive measures in the form of task performance for a user in a flow versus a user going out of flow;

[0050] Fig. 9 show an example graph of measured EEG power during a cognitive training task in stress resilience training, comparing a user being in a flow versus the user losing flow or finding the task easier;

[0051] Figs. 10-13 show examples of the effect to the EEG power when different stressors are presented to the user during cognitive tasks;

[0052] Fig. 14 shows an example of a a / p power ratio, where cognitive tasks are alternated with neurofeedback training periods;

[0053] Fig. 15A shows an example of the effect of a neurofeedback training session, where alpha EEG patterns are measured during cognitive tasks that are alternated with neurofeedback training periods;

[0054] Fig. 15B is a visualization of an analysis of the change in alpha levels in the form of a histogram of alpha levels during cognitive tasks; Fig. 16 shows an example of the effect of a neurofeedback training session, where alpha EEG patterns are measured during cognitive tasks that are alternated with neurofeedback training periods in a two-step stressor level approach;

[0055] Fig. 17 shows an example EEG pattern, in the form of a relative power a / p, obtained during a neurofeedback training session, and further indicating transitions between cognitive training tasks and neurofeedback training periods;

[0056] Fig. 18 shows an example EEG pattern, in the form of a relative power a / p, of a neurofeedback training period;

[0057] Figs. 19-20 show example EEG pattern, in the form of p power based relative power, of a part of a neurofeedback training session with cognitive training tasks;

[0058] Fig. 21 shows an example of a neurofeedback system;

[0059] Fig. 22 shows an example embodiment of a computing system for implementing certain aspects of the present technology; and

[0060] Figs. 23-25 show three example methods of three different optimizations according to the present disclosure.

[0061] The figures are intended for illustrative purposes only, and do not serve as restriction of the scope of the protection as laid down by the claims.

[0062] Detailed description

[0063] It will be readily understood that the components of the embodiments as generally described herein and illustrated in the appended figures could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of various embodiments, as represented in the figures, is not intended to limit the scope of the present disclosure but is merely representative of various embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0064] The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the present disclosure is, therefore, indicated by the appended claims rather than by this detailed description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present disclosure should be or are in any single example of the present disclosure. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, discussions of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same example.

[0065] Furthermore, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize, in light of the description herein, that the present disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present disclosure. Reference throughout this specification to "one embodiment," "an embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in an embodiment," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0066] Neurofeedback training teaches users to self-regulate brain activity that reflects specific mental states and cognitive functioning. The present disclosure is related to technology enabling neurofeedback training. Neurofeedback training may be performed where mental and cognitive performance influences the overall performance of a user. One area where neurofeedback training is particularly beneficial is in the world of sports as a means to improve athletes’ performance. Neurofeedback training sessions are usually organized on an individual basis but may be organized in groups.

[0067] Brain activity may be measured in the form of brain waves, such as alpha waves, beta waves and / or theta waves, which can be detected using an EEG. Different training goals and skills can be attributed to specific brain activity, which forms the basis of neurofeedback training. For example, a training goal of resilience to stress may be achieved by increasing beta wave activity, a training goal of being able to stay in a flow (staying calm during stress) may be achieved by increasing alpha wave activity, and a training goal of relaxation may be achieved by increasing theta wave activity.

[0068] Neurofeedback training involves a wearable device, worn on the head, with a wireless connection, e.g., a Bluetooth connection, to a computing device, such as a smart phone or any other computing device. The wearable device obtains real time EEG measurements through electrodes on the user’s head. The brain’s alpha, beta and theta activity may thus be measured. In neurofeedback training, operant conditioning procedures are used to self-regulate or control brain rhythms, and it is then investigated how these changes relate to changes in performance.

[0069] An example of a wearable device suitable for neurofeedback training takes the form of a flexible extension band with dry electrodes, an integrated electronic module and removable battery. The band is placed horizontally around the head (a top view of a head 100 is shown in Fig. 1 ) above the ears, so that the electrodes are roughly located above 10-20 positions according the International 10-20 system, e.g., at positions T3, T4, 01 and 02 as shown in Fig. 1 , and typically all referred to a ground located on the forehead. Alpha and beta wave signals from these electrodes may be sampled at a rate of, e.g., 250 Hz and transmitted via Bluetooth LE to the receiving computing device, such as a laptop or smart phone.

[0070] Neurofeedback sensory signals may be provided to the user performing the neurofeedback training in various manners. Non-limiting examples of neurofeedback sensory signals are changes in sounds by varying frequency ranges and / or amplitudes, e.g., in music; changes in light by changing frequencies and / or lumen values, e.g., of room lighting; changes in temperatures, e.g., room temperature and / or localized temperature on the body of the user, through a patch, using heating (e.g., infrared) elements and / or using cooling elements; changes in smell, e.g., by providing an odor to the room; and / or haptics feedback, e.g., through tactile stimulation. Another non-limiting example of a neurofeedback sensory signal may be related to feeling, e.g., provided by an airflow or change in airflow, such as wind speed and / or direction of the airflow presented to the user.

[0071] In the following examples, neurofeedback sensory signals will be described based on sound and in particular through music provided to the user through a headset, but it is to be understood that the present disclosure is not limited to this specific form of neurofeedback sensory signals.

[0072] EEG technology has been used for some in time medical applications and also in a variety of sports, mostly in individual sports such as golf and archery, and the general findings are that a decrease in brain activation is related to an increase in sports performance. This “neural efficiency hypothesis”, developed in the context of general intelligence, states that experts perform more effectively than beginners by only recruiting the brain areas needed to perform the task at hand, while at the same time inhibiting other brain areas that are not required.

[0073] Rhythmic EEG activity can be classified into several frequency bands, typically defined as alpha, beta, gamma, delta and theta frequency bands. The frequencies of each of these bands differs between studies; in the present disclosure the following definition is used, although other definitions may be user: delta (< 4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (> 30 Hz). Fast EEG rhythms are indicative of brain activation, as in intense mental activity; slow rhythms with brain inactivation, as in sleep. The intermediate alpha rhythm appears to reflect a special state of “relaxed wakefulness”. Its maximum can be seen over the posterior parts of the brain, and in resting state conditions it is almost always greater when participants have closed compared to open eyes. Alpha power in alpha EEG activity is inversely proportional to the activity of the underlying brain tissue. This makes measurement of alpha activity extremely suitable for application in the field of sports, where sports performance is linked to neural efficiency. If sports performance is linked to reduced brain activity, then this could be accompanied by an increase in EEG activity in the alpha band.

[0074] There is a lot of evidence supporting the positive relationship between sports performance and alpha activity. For example, alpha activity may be used as a dependent variable used as an index of the active inhibition of brain areas that are not required for the execution of a particular motor skill required for optimal sports performance. Alpha activity may also be used as an independent variable in which case the goal is to teach athletes to gain control over alpha activity so that they can activate or inhibit it as the situation requires. Besides improving sports performance, alpha activity has long been associated with positive effects on relaxation, sleep, and well-being. Fig. 2 shows an example of a neurofeedback training session 200, wherein various task are performed. Fig. 3 shows a neurofeedback training program 300 including a plurality of neurofeedback training sessions 200’, e.g., twenty neurofeedback training sessions 200, and optional assessments 302, 304 to measure changes in the cognitive performance as a result of the neurofeedback training sessions 200’.

[0075] A neurofeedback training session 200 may be performed on a computing device, in this example a smartphone, with a specially designed app that guides the user through the neurofeedback training session 200. A neurofeedback training session 200 may involve one or more (typically multiple) neurofeedback training periods 206 that may be alternated with one or more cognitive tasks 204, 208, 210, 212. It is to be understood that the specific cognitive tasks performed may be different from the ones shown in Fig. 2 and that the order of the cognitive tasks (alternated with the neurofeedback training periods 206) may be different from the example of Fig. 2.

[0076] A neurofeedback training period 206 is a period wherein an audio and / or visual signal, e.g., in the form of music, is relayed back to the user. Typically, the neurofeedback training period 206 is triggered by a reward threshold, i.e., the level of alpha and / or beta power at which the neurofeedback training period 206 is started. This threshold level may be fixed or variable. Another threshold level may determine when to end the neurofeedback training period 206. With regards to audio feedback, pleasant sounds are found to be more effective than unpleasant ones. For instance, audio feedback in the form of a sine wave leads to the production of more alpha compared to a sawtooth stimulus, because the sawtooth stimulus can act as a mild stressor leading to the suppression of alpha making it more difficult to enhance alpha via the neurofeedback training period 206. Providing participants with audio feedback that is rated as ‘‘highly pleasant” helps to relax, facilitating enhancement of alpha. With this knowledge, e.g., music may be adapted by filtering frequencies in the music to change the level of pleasantness (possibly unconsciously) perceived by the listener.

[0077] A cognitive task 204, 208, 210, 212 typically involves a so-called stressor task performed by the user to influence the alpha and / or beta levels of the user. To improve the performance of the user, it is a goal of the present disclosure to improve the relative power. For example, when training for resilience / coping with stress, it may be a goal to keep beta up during the stressor. For example, when training for flow / balance, it may be a goal to keep alpha up during the stressor. Other training purposes are possible. The reward threshold for triggering the neurofeedback training period 206 may depend on the progress made in a cognitive task 204, 208, 210, 212.

[0078] In the example of Fig. 2, the neurofeedback training session 200 may start and end with a visual-analog slider 202 by which the user indicates his / her level of drowsiness, relaxation, and boredom, e.g., on a scale from 0 to 100. The app may then continue with a collection of, e.g., two periods of 2 minutes resting-state EEG 204, in which the user may be sitting still with eyes open and eyes closed, respectively. This may be followed by, e.g., four neurofeedback training periods 206 of, e.g., 5 minutes each, which may be alternated with gamified cognitive tasks 208, 210, 212 that last, e.g., 3 minutes each. The gamified cognitive tasks are, e.g., a switching task 208, a psychomotor vigilance task 210 and a mental rotation task 212, preferably always performed in the same order. A single neurofeedback training session 200 lasts, e.g., about 45 minutes in total. A neurofeedback training session 200 may include instructions to, e.g., just “sit back, relax and listen to the music”. For the task periods 208, 210, 212 the user may be asked to execute the tasks as quickly and as accurately as possible.

[0079] The duration of a neurofeedback training session 200 may be different and consequently the number of cognitive tasks in the neurofeedback training session may be different, typically shorter than 45 minutes such as 10 minutes or 20 minutes. For example, a neurofeedback training session with a duration of 10 minutes may include two neurofeedback training periods 206 and one or two cognitive tasks.

[0080] During each of the neurofeedback training periods 206 and / or cognitive tasks 204, 208, 210, 212, the EEG brain waves may be measures, e.g., to obtain alpha, beta and / or theta levels, using the wearable device. In the present disclosure, the focus is on the alpha and beta levels.

[0081] A relative power may be calculated from the EEG power spectrum, based on the spectra of the various brain waves.

[0082] A relative power may be calculated as the measured power (or the sum of this power) in the alpha band (8-12 Hz) divided by the measured power (or the sum of this power) in the beta band total power (13-30 Hz), which may be expressed as a / p. The relative power may be expressed differently, e.g., including a factor ‘a’ or a factor ‘b’ in one or both of the measured alpha power and measured beta power, the relative power then being expressed as, e.g., aa / p, a / b or aa / bp. Alternatively, the relative power may be determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power. In the present disclosure, an alpha power based relative power is a relative power that is calculated with an alpha power in the numerator of the power ratio.

[0083] Alternatively, the relative power may be inversely calculated and expressed, e.g., as p / a, possibly including a factor. In the following examples, the a / p power ratio will be used as an example of the relative power. Alternatively, the relative power may be determined by calculating a ratio of beta power over a total brainwave power or over another brain wave power. In the present disclosure, a beta power based relative power is a relative power that is calculated with a beta power in the numerator of the power ratio.

[0084] If two electrode montages (e.g., left T3-O1 and right T4-O2 shown in Fig. 1 ) for measuring alpha levels or beta levels, respectively, show a good epoch of, e.g., 4 s, then an average of the electrodes may be used. If only one electrode montage yields a good epoch, then only that channel may be used. If neither electrode montage results in a good epoch, then no feedback update may be given.

[0085] The relative alpha measure may be filtered by a first-order infinite impulse response (HR) filter with a time constant of, e.g., 4 s. In this way, the speed of the changes in the feedback was smoothed somewhat, and do not go back and forth too quickly.

[0086] The neurofeedback training period 206 may involve an EEG alpha training, wherein the user listens to music, e.g., own favorite music, that the user may have selected before the start of the neurofeedback training session 200. Preferably, the user listens to the music using earplugs or headphones. The music may be passed through a high-pass filter that removes the low frequencies in the music based on the EEG alpha level of the brain signals. For example, the lower the level of alpha activity, the more low frequencies may be filtered out. This makes the music sound distant and superficial if the alpha level is low, and full and rich when the alpha level is high, thus providing an intuitive feedback on the EEG alpha level based on the quality or pleasantness of the music. It is known that, e.g., five times per second (each, e.g., 200 ms) a segment of the preceding 4 seconds of EEG data may be filtered by fifthorder Butterworth filters at, e.g., 1 Hz high-pass and, e.g., 65 Hz lowpass, and a second-order notch filter at, e.g., 50 Hz. To be usable for a feedback update, this segment may fulfil some criteria, e.g., (i) no clipping or overflow of the amplifier, (ii) peak-to-peak of at most 200 pV, (iii) the ratio between the line noise (49-51 Hz) and EEG power (e.g., 4-30 Hz) be smaller than 1 .0.for each electrode.

[0087] A feedback measure may be used to drive the cut-off frequency of a first-order high-pass filter built into the audio path of the music played through the headphones. This cut-off frequency equals, e.g., 2 Hz if the current alpha level is greater than the maximum alpha level for the previous part, and, e.g., 1500 Hz if the current alpha level is lower than the minimum alpha level for the previous part. For intermediate levels, a linear interpolation may be performed in such a way that the cut-off frequency is high for low current alpha levels and low for high alpha levels.

[0088] A non-limiting example of a switching task 208 is a gamified switching task called “deluge of dice”, played on the smartphone. A non-limiting example of a psychomotor vigilance task 210 is a gamified psychomotor vigilance task called “react-o-matic”, which is a simple reaction time task focused on response speed. A non-limiting example of a mental rotation task 212 is a gamified mental rotation task called “typo trap”. Other or additional gamified cognitive tasks 208, 210, 212 may be implemented.

[0089] To assess the progress of the neurofeedback training sessions 200’, a neurofeedback training program 300 may include an assessment session 302 prior to the neurofeedback training sessions 200’ and another assessment session 304 after the neurofeedback training sessions 200’. Each assessment session 300, 304 may involve one or more cognitive tasks, for example an N-back task, a stop-signal task and an attention network task (ANT) performed in this order, or any other suitable cognitive tasks, preferably including cognitive tasks based on gamification. The cognitive tasks may be performed on a computing device, in this example a laptop.

[0090] The present disclosure presents three optimization solutions for a neurofeedback training session 200, typically with the objective to maximize the impact of a resilience training or to maximize the impact of a flow training to as user, while making effective use of the neurofeedback training session time duration. The neurofeedback training session 200 may be individualized to the user, e.g., personalized based on individual measurements. The three optimization solutions may be used in isolation, but a combination of (preferably all) optimization solutions may achieve best results. The first optimization according to the present disclosure involves detecting an EEG pattern and acting on this during a neurofeedback training period 206, in the following example a music interval 206. Traditionally, adaptation of neurofeedback settings and assessment of user performance is based around exceeding thresholds in the power spectra. However, it has been found that in some cases the absolute scores obtained from power spectra do not provide adequate information, as different users show markedly different time dependence in their power spectra across a session. The first optimization includes an improved use of EEG patterns during the neurofeedback training period 206, such as the pattern of Fig. 4, enabling a more effective use of session time to ultimately improve the impact of a neurofeedback training session and the user performance.

[0091] Fig. 4 shows an example EEG pattern 400 obtained during a neurofeedback training period 206, such as a music interval 206. Note that the EEG pattern 400 is a simplified, i.e., smoothened representation of an EEG pattern, which in reality will be much more capricious. The x-axis represents time and the y-axis represents a relative power, in this example expressed as a / p, i.e., the ratio of a power over p power. Three phases are identified in the a / p power profile: a rise period 402, a plateau period 404 and a fall period 406. The rise period 402 corresponds to a period where the user is experiencing a learning effect. During the plateau period 404 the user experiences a sustained less attention effectiveness. The fall period 406 corresponds to a period where the user is losing engagement.

[0092] The rise period 402 may be characterized, and therewith detected, based on a number of variables, such as its starting a / p value, the rise time and / or the rise slope, but also on contextual data, such as prior history data and / or session context. These characteristics may be used as neurofeedback data for this neurofeedback training period 206 or for subsequent training tasks in the neurofeedback training session 200.

[0093] An example of a rise period 600 is presented in Fig. 6. Rise period 600 is an example of rise period 402 of Fig. 4. The rise period 600 may include a rise delay time A followed by one or more rise times T, such as n, 12 in Fig. 6. The rise delay time A defines a delay in the relative power, in this example power ratio a / p, before the effect of the neurofeedback training causes an increase in the power ratio a / p. The rise delay time A may be defined as the time to the point TO in the curve 600 where the rate of change of power ratio a / p starts to increase. At the point TTthere is a rise time tipping point where the increase in rise changes into a decrease in rise. The rise time tipping point TTis therefore related to a change in sign of a second order differential or other function reflecting this point in the curve 600.

[0094] The rise time(s) T defines a characteristic rise time in the power ratio a / p as a result of the neurofeedback training. In the example of Fig. 6, at then end of n the increase in power ratio a / p starts to decrease and at then end of T2 the increase in power ratio a / starts to level out. R1 and R2 represent the initial and final power ratio values at, respectively, the start and end of the rise in power ratio a / p. The rise time(s) T may be related to the integration time (IT) used to reduce noise in the power ratio signal. For example, it may be determined that: if IT < T then a real time response is possible; and if IT > T then a real time response is not possible. This determination may be translated into parameters for use in subsequent training tasks.

[0095] The end of the rise period 600, and therewith the start TP of the plateau period, such as plateau period 404, may be determined by taking the derivative of the EEG pattern or any other function to determine that the rise time becomes substantially zero.

[0096] The plateau period 404 may be characterized, and therewith detected, based on variables such as its sustainable a / p level and / or the duration, but also on contextual data, such as session context. These characteristics may be used as neurofeedback data for this neurofeedback training period 206 or for subsequent training tasks in the neurofeedback training session 200. The plateau duration (or plateau length) is an important indication of the time that the user can sustain focus (in the flow), which may be a training goal to maximize. A good determination of the plateau period 404 is therefore important. In order to determine the plateau duration 404, the start of the plateau period 404 (depicted T in Fig. 4) and the start of the fall period 406 (depicted TF in Fig. 4) may be determined.

[0097] In order to keep the user in the plateau period 404 or even get the user to one or more higher plateau periods 504, as illustrated in Fig. 5, the music feedback presented to the user during the neurofeedback training period 206 may be adapted in real time, e.g., by changing audio frequencies, tempo and / or momentum.

[0098] The plateau period 404 typically includes a capricious EEG pattern 700 such as shown in the example of Fig. 7. The rises and falls in this EEG pattern 700 should not be confused with a rise period 402 or a fall period 406. The rises and falls in the EEG pattern 700 may be referred to as lapses, which may be caused by user’s distractions.

[0099] In an embodiment, a statistical model may be used to obtain a trend line 702. Variations and trend data from the EEG pattern 700 may be used to determine the duration and / or strength of the trend 702 and therewith of the plateau period 404.

[0100] In an embodiment, an a / p plateau level may be determined based on the spread in the power ration a / p around the median value in the EEG pattern 700. The spread may be determined based on a histogram having a normal distribution around the median, wherein for the a / p plateau level, e.g., only values between the 15% and 85% percentiles are taken into consideration. Thus, excessive EEG values may be filtered from the EEG pattern 700.

[0101] The fall period 406 may be characterized, and therewith detected, based on a number of variables, such as its starting a / p value, the fall time and / or the fall slope, but also on contextual data, such as session context. These characteristics may be used as neurofeedback data for this neurofeedback training period 206 or for subsequent training tasks in the neurofeedback training session 200. When the user loses engagement, the neurofeedback training period 206 becomes less effective and this may trigger a cognitive task 204, 208, 210, 212 to be performed earlier.

[0102] As described above, for the plateau period 404, the a / p plateau level may be based on the distribution around the median of the EEG pattern. In an embodiment, the start of the fall period 406, i.e., at TF in Fig. 4, may be determined at when the median of the histogram starts to decrease.

[0103] As indicated above, the characteristics of the EEG pattern 400, and in particular that of the rise period 402, the plateau period 404 and the fall period 406 may be used for personalizing / changing parameters of the current neurofeedback training period 206 or for personalizing / changing parameters of a next training in the neurofeedback training session 200. The neurofeedback data may include any information obtained from the EEG pattern 400, 600, e.g., as described above for the periods 402, 404, 406, 504, 600. For example, the neurofeedback data may include: the starting time of the neurofeedback training period 206; the rise delay time A; the rise time tipping point TT; the start time TP of the plateau period 404; the amount of lapses (i.e., distractions) during the plateau period 404; the trend (i.e., spread) and duration of the plateaus 404, 504; the amount of plateaus 404, 504; the start of the fall TF; and / or the end time of the neurofeedback training period 206. The parameters for a next neurofeedback training period 206 or a next cognitive task 204, 208, 210, 212 or a next session 200 may further be based on subjective information obtained from the user, e.g., via a user’s evaluation.

[0104] The neurofeedback data obtained from the EEG pattern 400 may be used to calculate a score, which score may be used to change one or more parameters used in a next neurofeedback training period 206 and / or next cognitive task 204, 208, 210, 212 in the neurofeedback training session 200.

[0105] In an example embodiment, the length of a neurofeedback training period 206 may be based on the rise time 402, 600, corresponding to the start time TP of the plateau period 404. When, e.g., a training goal is resilience to stress, the length of the neurofeedback training period 206 may be set to A / x TP, preferably with 0< / <1 . When, e.g., a training goal is to stay in a flow, the length of the neurofeedback training period 206 may be set to N x TP, preferably with A / >1. The value of N may be determined based on the training goal, the analysis of relative power measurements and / or historic measurements.

[0106] The neurofeedback data may be obtained from the neurofeedback training period 206 in a predetermined time frame, e.g., per 3 minutes. The value of N may be limited to this predetermined time frame.

[0107] The second optimization according to the present disclosure involves detecting an EEG pattern and acting on this during a cognitive task 204, 208, 210, 212. Cognitive tasks may be used to induce stress in a user, for example in order to increase the user’s resilience or to aid them to remain in a flow. Task induced stress may be measured in the user’s brain power levels as well as in the user’s task performance. Traditionally, the main attention is on a single task. The second optimization of the present disclosure allows the user’s power spectra to be used across different cognitive tasks and in particular for settings the parameters of subsequent tasks.

[0108] Fig. 8 illustrates two graphs of cognitive measures in the form of task performance 800 for a user in a flow 802 versus a user going out of flow 804. The x- axis represents time; the y-axis represents the task performance in any performance measure. When the task performance stays high, as in the graph 802, it may be concluded that the user is in a flow. When the task performance becomes lower, as in the graph 804, it may be concluded that the person gets out of flow and the task is to be aborted.

[0109] From the measured EEG power from which the relative power has been determined, the user being in a flow versus the user losing flow may be detected based on the pattern of the EEG power in time. An example of this is illustrated in Fig. 9, where the x-axis represents time and the y-axis represents the measured EEG power 900 for a user in a flow 902 and a user getting out of flow 904. A transition point 906 in time can be detected where the user gets out of flow by a change in the slope of the EEG power. When an abort pattern 904 is detected in the EEG power, it may be decided to stop or change the current cognitive task the user is performing.

[0110] Different type of cognitive tasks 208, 210, 212 may be performed, depending on the training’s purpose. The pattern of EEG power 900 may be different depending on the cognitive task being performed.

[0111] For example, when training for resilience, the cognitive task may keep increasing the stress level and the EEG pattern should show continuous high deviation around a high beta power based relative power.

[0112] For example, when training for flow, the cognitive task may keep stress constant and the time may be measured until the flow gets lower. The difficulty of the tasks may be in balance with the skill level of the user. The EEG pattern should show a constant or medium deviation around a high alpha power based relative power.

[0113] In an example embodiment of cognitive tasks 208, 210, 212 for training resilience, the beta power may be measured and a beta power based relative power may be analyzed. In order to achieve higher resilience, the goal of the cognitive tasks may be to maintain the beta power based relative power level at a constant level at the end of the cognitive task. Fig. 10 and Fig. 11 show the effect of different stressors presented to the user during such cognitive tasks.

[0114] Fig. 10 shows an example of a beta power based relative power 1000 where the level of stressor is kept constant during cognitive tasks 1002, which may be one or more of the cognitive tasks 208, 210, 212, for training resilience. The cognitive tasks 1002 are alternated with neurofeedback training periods 206, where, typically, the beta levels get lower and alpha levels increase. In the example of Fig. 10, the beta power based relative power 1000 shows three peaks 1004, 1006, 1008 that get lower as a result of presenting the user with three same or similar level stressors during the cognitive tasks 1002. The beta power based relative power 1004, 1006, 1008 reduces as the user finds the task more familiar / easier with each repeated stressor 1002. This reduction in beta power is undesired. The results of an improved neurofeedback training session for training resilience is shown in Fig. 11 .

[0115] Fig. 11 shows an example of a beta power based relative power 1100 where the level of successive stressors is changed during the cognitive tasks 1102, 1104, 1106, which may be one or more of the cognitive tasks 208, 210, 212, for training resilience. The cognitive tasks 1102, 1104, 1106 are alternated with neurofeedback training periods 206, where, typically, the beta levels get lower and alpha levels increase. In the example of Fig. 11 , the beta power based relative power 1100 shows three peaks 1108, 1110, 1112 that remain substantially the same as a result of presenting the user with three different stressors during the cognitive tasks 1102, 1104, 1106. The beta power based relative powers 1108, 1110, 1112 remain substantially the same as, in this example, the user finds the task more difficult with each successive stressor during the respective tasks 1102, 1104, 1106. Le., in this example, the second stressor 1104 is larger / more intense than the first stressor 1102, and the third stressor 1106 is larger / more intense than the second stressor 1104. By measuring the beta levels in the EEG pattern and determining the relative power, the stressor tasks in the cognitive tasks may be adapted or parameterized such that the beta levels, e.g., resulting in the peak beta level related relative powers 1108, 1110, 1112, are kept substantially at a same level. Alternatively, an average beta level or trend in beta level may be calculated and monitored to be kept at a substantially same level over time.

[0116] In another example embodiment of cognitive tasks 208, 210, 212 for training resilience, the alpha and beta power may be measured to obtain the a / p power ratio, which may be analyzed to optimize the cognitive tasks. In order to achieve higher resilience, the goal of the cognitive tasks may be to maintain a relatively low a / p power ratio at the end of each of the cognitive tasks. Fig. 12 and Fig. 13 show the effect of different stressors presented to the user during such cognitive tasks.

[0117] Fig. 12 shows an example of a a / p power ratio 1200 where the level of stressor is kept constant during cognitive tasks 1202, which may be one or more of the cognitive tasks 208, 210, 212, for training resilience. The cognitive tasks 1202 are alternated with neurofeedback training periods 206, where, typically, the beta levels get lower and alpha levels increase. In the example of Fig. 12, the a / p power ratio 1200 increases, which may be determined, e.g., from the increasing dips 1204 in the a / p power ratio or from calculating an average a / p power ratio. This increase is a result of presenting the user with three same or similar level stressors during the cognitive tasks 1202. The a / p power ratio 1200 increases as the user finds the task more familiar / easier with each repeated stressor 1202, typically resulting in the beta levels to increase less within a session. This increase in a / p power ratio is undesired. The results of an improved neurofeedback training session for training resilience is shown in Fig. 13.

[0118] Fig. 13 shows an example of a a / p power ratio 1300 where the level of successive stressors is changed during the cognitive tasks 1302, 1304, 1306, 1308, which may be one or more of the cognitive tasks 208, 210, 212, for training resilience. The cognitive tasks 1302, 1304, 1306, 1308 are alternated with neurofeedback training periods 206, where, typically, the beta levels get lower and alpha levels increase. In the example of Fig. 13, the a / p power ratio 1300 remain substantially the same as a result of presenting the user with four different stressors during the cognitive tasks 1302, 1304, 1306, 1308. The a / p power ratio 1300 remains substantially the same as, in this example, the user finds the task more difficult with each successive stressor during the respective tasks 1302, 1304, 1306, 1308. Le., in this example, the second stressor 1304 is larger / more intense and therefore may have a shorter duration than the first stressor 1302, the third stressor 1306 is larger / more intense and therefore may have a shorter duration than the second stressor 1304, and the fourth stressor 1308 is larger / more intense and therefore may have a shorter duration that the third stressor 1306. By measuring the a / p power ratio 1300, the stressor tasks in the cognitive tasks may be adapted or parameterized such that the a / p power ratio 1300 is kept substantially at a same level. The a / p power ratio 1300 may be measured, e.g., by comparing the peaks and dips of the a / p power ratio 1300, or by calculating an average a / p power ratio 1300 that is to remain substantially constant over time during the cognitive tasks.

[0119] In an example embodiment of cognitive tasks 208, 210, 212 for training flow, the alpha power may be measured and the alpha power based relative power may be analyzed. In order to achieve better flow, the goal of the cognitive tasks may be to keep the alpha level at a high level during a stressor of a cognitive task. Figures 14- 16 show the effect of a training session with a stressor to improve flow. Fig. 14 shows an example of a a / p power ratio 1400, where cognitive tasks 1402 are alternated with neurofeedback training periods 206. The level of the stressor is initially set to a first level. With each successive cognitive task 1402 it is an aim to keep alpha levels high during the stressor, i.e., to be able to be calm during the stressor. When the user is able to keep calm during the stressor task, then the user is in the so-called flow situation. If flow is lost, the alpha level typically reduces during the cognitive task 1402, and herewith the a / p power ratio 1400, and typically only recovers during a neurofeedback training period 206. The user repeats the cognitive training periods 1402 resulting in the task becoming less stressful and resulting in a better flow.

[0120] Fig. 15A shows an example of the effect of a neurofeedback training session 200, where alpha EEG patterns are measured an alpha based relative powers are determined during two cognitive tasks 1402 that are alternated with neurofeedback training periods 206, such as shown in Fig. 14. Performing a cognitive task 1402 may initially result in a change Aa in alpha level based relative power as shown in the alpha based relative power pattern 1502. Repeating the cognitive task 1402 one or more times results in the change Aa in alpha based relative power levels to become less as shown in the pattern 1504. By repeating the cognitive tasks 1402, alternated with the neurofeedback training periods 206, the alpha level based relative powers get closer with less de deviation to and may be kept substantially at a maximum alpha level.

[0121] With each cognitive task 1402 being performed, the change Aa in alpha levels may be measured. Between cognitive tasks 1402, the change Aa in alpha levels may be compared. Fig. 15B visualized an analysis of the change Aa in alpha levels in the form of a histogram of alpha levels during the cognitive tasks. With the aim of keeping the alpha levels at a maximum level amax, the deviation in alpha levels may be analyzed, where lower values represent a loss of flow.

[0122] In Fig. 15B this is visualized by the a / p power ratio being plotted along the x-axis and each bar representing the number of time n that the a / power ratio occurs during the cognitive tasks 1402. Along the x-axis, the distance between amax and a bar represents the value of Aa. When the user gets in a flow, the bars near or at amax will rise, i.e., n increases, indicative of a good flow. On the other hand, when n starts to rise at lower a / p power ratios, i.e., Aa becomes larger, then the user gets out of flow. Based on this analysis parameters of the neurofeedback training periods 206 and / or the cognitive tasks 1402 may be adjusted to get the user in the flow.

[0123] For example, a user may perform cognitive tasks 1402 at stressor level ST1 and an alpha power based relative power histogram as shown in Fig. 15B may be recorded. The asymmetrical a / p power ratio distribution, such as visualized in Fig. 15B, may change in width of the histogram indicative of changes in stress level. As the flow of the user improves, the histogram becomes narrower, i.e., having a / p power ratios closer to amax and Aa in Fig. 15B becoming smaller.

[0124] The latter is visualized in Fig. 16, where in the top for three alpha based relative powers are plotted with an evolving histogram characteristics evolving from H1 to H2 to H3. The histograms itself are plotted below the graph, showing that the histograms become narrower from H1 to H2 to H3.

[0125] When the user reaches a predefined histogram, e.g., having a predefined a / p power ratio distribution measured over a number of successive cognitive tasks 1402, e.g., corresponding with H1 in Fig. 16, the cognitive tasks 1402 may be adjusted by increasing the stressor to a next level ST2. The neurofeedback training session 200 may then be continued with this adjusted level ST2 and the a / p power ratio may be analyzed again with the aim to get into the flow again with the increased stressor level ST2. An example resulting alpha based relative power graph for three repeated cognitive tasks are shown in the lower graph of Fig. 16, depicted H2I , H22 and H23. As with the first graph, histogram characteristics may be followed, in this case evolving from H2I to H22 to H23. The histograms itself are plotted below the graph again, showing that the histograms become narrower from H2I to H22 to H23.

[0126] Thus, the user may be requested to perform any number of cognitive tasks with different stressors, such as ST1 and ST2, until, e.g., a training goal has been reached or a determined training time has ben reached.

[0127] Thus, by repeatedly performing cognitive tasks 1402 until the user gets into a flow and repeating the cognitive tasks 1402 at a higher level when the used got into the flow, the performance of the user regarding flow may be improved.

[0128] The third optimization according to the present disclosure involves detecting an EEG pattern during a neurofeedback training session 200 (i.e., including neurofeedback training periods 206, e.g., music intervals 206, and cognitive training periods 204, 208, 210, 212) and acting to optimize the sequence and duration of the alternating neurofeedback training periods 206 and cognitive training periods 204, 208, 210, 212 given a maximum acceptable neurofeedback training session 200 duration. The moments of transition between neurofeedback training periods and cognitive training periods plays an important role in the third optimization.

[0129] To get the most benefit from a neurofeedback training session 200 or a neurofeedback training program 300, one or more of the following may be assessed: the termination time for a neurofeedback training period 206; the termination time for cognitive tasks / training periods 204, 208, 210, 212, 1002, 1102, 1104, 1106, 1202, 1302, 1304, 1306, 1308, 1402; neurofeedback settings for use during neurofeedback training periods 206; cognitive task settings (e.g., stressor levels, and etcetera) for use during cognitive training periods; a choice of task / training to transition to after a current task / training; or any other variable or parameter influencing the neurofeedback training session 200. In any event, the goals of the training, e.g., resilience or flow, are considered.

[0130] Fig. 17 shows an example of a neurofeedback training session 200 in the form of a neurofeedback training session 1700. The a / p power ratio of three neurofeedback training periods Ti, T2 and T3 and two cognitive training periods T’1 and T’2 are shown. The dotted vertical lines indicate transitions between neurofeedback training periods and cognitive training periods. An analysis may be performed to obtain optimal times for transitioning between neurofeedback training periods and cognitive training periods.

[0131] As an example, a transition may be based on faster improvement in relaxation. The aim may then be to have T2<TI.

[0132] As an example, a transition may be based on being able to perform a stressor task more effectively to be able to stay in a flow. The aim may then be to have T’2<T’I.

[0133] Other non-limiting examples of transition goals may be better task / relaxation performance, determining a next interval to be a neurofeedback training period or a cognitive training period, improving resilience, and / or improving flow maintenance.

[0134] Parameters or settings that may be used to determine transition moments include reward levels, threshold levels and / or levels of difficulty of tasks.

[0135] In an example, it may be a goal to maximize the impact of resilience training during a neurofeedback training period 206. Typically, the period of neurofeedback training is minimized between stressor tasks. Traditionally, it is difficult the determine when to terminate a neurofeedback training period 206 whilst ensuring sufficient that neurofeedback training effect has been reached, as different users require different periods of neurofeedback training before alpha power based relative power maximizes. The third optimization of the present disclosure aims to correctly or most optimally determine the termination time of a neurofeedback training period, e.g., to determine the time of transition between Ti and T’i in Fig. 17 in case of a resilience training.

[0136] With reference to Fig. 18, to determine the transition in case of resilience training, a plateau rise time Tp, i.e., the moment where the a / p power ratio levels out at a maximum, may be determined and used as a reference. The transition time for terminating the neurofeedback training period and transition to a cognitive training period may be determined based on the plateau rise time Tpand a predefined period ”X” for maintaining the plateau level, i.e., the plateau may be maintained for X x Tp. The total time of the neurofeedback training period may thus be determined as (1 +X) x Tp. For resilience training, preferably X is a small number, e.g., 0<X<1 .

[0137] Note that also shown in Fig. 18 are a start time Tsof the neurofeedback training period 206, a TA where the rise time(s) T starts and a fall time TF where the a / p power ratio starts to drop, which, together with Tp, may be determined based on the first optimization of the present disclosure.

[0138] In an example, it may be a goal to maximize the impact of flow training during a neurofeedback training period 206. Typically, the period of neurofeedback training is longer between stressor tasks. Traditionally, it is difficult to determine when to terminate the neurofeedback training period 206 whilst ensuring sufficient neurofeedback training effect has been reached, as different users require different periods of neurofeedback training before alpha power based relative power maximizes. The third optimization of the present disclosure aims to correctly or most optimally determine a correct termination time of the neurofeedback training period, e.g., to determine the time of transition between Ti and T’i in Fig. 17 in case of a flow training.

[0139] Similar to the previous example related to resilience, with reference to Fig. 18, to determine the transition in case of flow training, a plateau rise time Tp, i.e., the moment where the a / p power ratio levels out at a maximum, may be determined and used as a reference. The transition time for terminating the neurofeedback training period and transition to a cognitive training period may be determined based on the plateau rise time Tpand a predefined period ”X” for maintaining the plateau level, i.e., the plateau may be maintained for X x Tp. The total time of the neurofeedback training period may thus be determined as (1 +X) x Tp. For flow training, preferably X is a large number, e.g., X>1 .

[0140] In an example, it may be a goal to maximize the impact of resilience training during a cognitive training period, typically involving one or more stressor tasks. Typically, the cognitive training period is minimized before starting a next neurofeedback training period 206. Traditionally, it is difficult to determine when to terminate the stressor whilst ensuring sufficient stress effect has been reached, as different users require different stressor periods before beta power based relative power maximizes. The third optimization of the present disclosure aims to correctly or most optimally determine the time to stop the cognitive training period, e.g., in the form of a stressor termination time, and to transition, e.g., between T’i and T2 in Fig. 17 in case of a resilience training.

[0141] Fig. 19 shows an example beta power based relative power over time for a neurofeedback training period 1902 followed by a cognitive training period 1904. The transition moment 1906 may be determined based on the duration of the neurofeedback training period of (1 +X) x Tpas described above. To determine the time to transition out of the cognitive training period 1904 in case of resilience training, a plateau rise time TSPof the stressor may be determined, wherein TSPis the time to reach beta power based relative power plateau. TSPmay then be used as a reference to determine when the cognitive task is to be terminated, which may be based on a predefined period of time “Y” for maintaining the plateau level, i.e., the plateau may be maintained for Y x TSP. The total time of the cognitive training period may thus be determined as (1 +Y) x TSp. For resilience training, preferably Y is a small number, e.g., 0<Y<1.

[0142] In an example, it may be a goal to maximize the impact of flow training during a cognitive training period, typically involving one or more stressor tasks. Typically, the period of the cognitive training period is maximized before the next neurofeedback training but only if the stressor remains stressful. Traditionally, it is difficult to determine when to terminate the stressor interval whilst ensuring that the stressor remains stressful, as for different users stressors remain stressful for different periods. The third optimization of the present disclosure aims to correctly or most optimally determine the time to stop the cognitive training period, e.g., in the form of a stressor termination time, and to transition, e.g., between T’i and T2 in Fig. 17 in case of a flow training.

[0143] Fig. 20 shows an example beta power based relative power over time for a neurofeedback training period 2002 followed by a cognitive training period 2004 at the transition moment 2006. To determine the time to transition out of the cognitive training period 2004 in case of flow training, a plateau fall time TSF of the stressor may be determined, wherein TSF is the time where the beta power based relative power plateau cannot be maintained, i.e., the beta level starts to lower after the plateau. At the same time, the alpha levels may be monitored. Thus, it may be determined that the user ceases to find the stressor stressful, as further detailed for the second optimization of the present disclosure. TSF may then be used as transition time to transition to a next neurofeedback training period.

[0144] In an example embodiment, the neurofeedback training session may use a transition to transition between two different neurofeedback training periods 206. If, e.g., alpha power based relative power drops during a neurofeedback training session, another neurofeedback training period 206 may be started wherein, e.g., another music track, an altered music track, other visual stimulations, or any other changed neurofeedback sensory signal is presented to the user. In another example, it may be determined that the alpha power based relative power fluctuates, which may trigger a transition to another neurofeedback training period with altered neurofeedback sensory signal(s).

[0145] The above-described optimizations according to the present disclosure may be implemented in a neurofeedback system, an example of which is shown in Fig. 21. Neurofeedback system 2100 may include a processing system 2102 which may have an input apparatus configured for capturing at least one bio-signal of a user. The input apparatus may itself be a sensor that is arranged for capturing the at least one biosignal, or it may be an interface coupled to an external sensor in order to receive and thus capture the at least one bio-signal from that external sensor. In the context of the present disclosure, the bio-signal includes at least a neurofeedback sensory signal. Processing system 2102 may further include a processing module configured for adjusting a sensory signal, i.e., neurofeedback sensory signal, relative to a default setting of the sensory signal. The processing module may, e.g., be a logical software module incorporated on an electronic system comprising a processor and a memory. Processing system 2102 may further include a signal interface configured for outputting the adjusted sensory signal to a neurofeedback playback device 2104. The signal interface may, e.g., include a transmitter configured for transmitting the signal to the neurofeedback playback device, or it may be directly coupled to the neurofeedback playback device. The neurofeedback playback device may in a practical implementation be arranged for being perceivable to the user, indicated by the dashed line. The neurofeedback playback device may, e.g., be an audio speaker or a video display or a combination thereof. Alternatively or additionally, the neurofeedback playback device may, e.g., be a light generating system, dimmable light and / or a haptic device configured for tactile stimulation of the user and / or any other device capable of outputting a sensory output that the user can perceive with one or more of his / her senses. If the neurofeedback playback device is a haptic device such as a smartphone, then it is preferred to couple a vibration function of the haptic device to one or more bio-signal of the user, e.g., the breath rhythm of the user, in order to further improve the biofeedback.

[0146] In an embodiment, the neurofeedback playback device may be part of, or integrated in, an enclosed multisensory environment, such as a sensory pod or cabin, configured to simultaneously present multiple types of sensory signals to the user, including but not limited to audio, light, temperature, airflow, scent, and haptic stimulation.

[0147] The processing module of the processing system 2102 may be configured for said adjusting by: determining at least one characteristic based on the at least one bio-signal and subsequently adjusting the sensory signal based on the at least one characteristic, using a machine learning procedure or based on an output from a pretrained machine learning system.

[0148] The processing system 2102 serves for biofeedback, in the sense that it takes a bio-signal from a user and outputs an adjusted sensory signal, which can be directed to that user.

[0149] A user 2106 may wear a wearable device 2108 including an EEG sensor, or any type of sensor capable of sensing at least the desired type of bio-signal for processing system 2102. One or more sensors may be used. The wearable device 2108 may be connected to processing system 2102, e.g., to an input apparatus of system 2102, in order to provide the at least one bio-signal to system 2102.

[0150] An intermediary device 2110 may be part of the neurofeedback system 2100, e.g., in the form of a smartphone or a personal computer. The intermediary device 2110 may relay the at least one bio-signal to system 2102. In an example, a media library 2112 may optionally be stored on intermediary device 2110, and which may hold preferred media content of the user 2106. Media library 2112 may, e.g., be a personal music or video or multimedia library on a smartphone or on a network attached storage in a local area network of the user 2106. Alternatively, media library 2112 may be stored elsewhere, e.g., in the cloud, as a streaming media library of the user 2106. Media library 2112 may be connected to processing system 2102, in particular to a processing module of the processing system 2102, in order to provide a default setting of the sensory signal. It will be appreciated that this advantageously allows processing system 2102 to operate on preferred media content of the user 2106, thus improving feelings of comfort for the user 2106.

[0151] Fig. 22 shows an example embodiment of a computing system 2200 for implementing certain aspects of the present technology. In various examples, the computing system 2200 may be any computing device making up any part of the neurofeedback system 2100 of Fig. 21 , or any other computing system described herein.

[0152] In some implementations, a computing system 2200 may implement the methods described herein, such as method 2300, method 2400 or method 2500 of the present disclosure.

[0153] The computing system 2200 may include any component of a computing system described herein, which components may be in communication with each other using connection 2205. The connection 2205 may be a physical connection via a bus, or a direct connection into processor 2210, such as in a chipset architecture. The connection 2205 may also be a virtual connection, networked connection, or logical connection.

[0154] In some implementations, the computing system 2200 may be a distributed system in which the functions described in this disclosure may be distributed within a datacenter, multiple datacenters, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the functions for which the component is described. In some embodiments, the components may be physical or virtual devices.

[0155] The example system 2200 includes at least one processing unit (CPU or processor) 2210 and a connection 2205 that couples various system components including system memory 2215, such as read-only memory (ROM) 2220 and randomaccess memory (RAM) 2225 to processor 2210. The computing system 2200 may include a cache of high-speed memory 2212 connected directly with, in close proximity to, or integrated as part of the processor 2210.

[0156] The processor 2210 may include any general-purpose processor and a hardware service or software service, such as services 2232, 2234, and 2236 stored in storage device 2230, configured to control the processor 2210 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 2210 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

[0157] To enable user interaction, the computing system 2200 may include an input device 2245, which may represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. The computing system 2200 may also include an output device 2235, which may be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems may enable a user to provide multiple types of input / output to communicate with the computing system 2200. The computing system 2200 may include a communications interface 2240, which may generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

[0158] A storage device 2230 may be a non-volatile memory device and may be a hard disk or other types of computer readable media which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs), read-only memory (ROM), and / or some combination of these devices. The storage device 2230 may include software services, servers, services, etc., that, when the code that defines such software is executed by the processor 2210, causes the system to perform a function. In some embodiments, a hardware service that performs a particular function may include a software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 2210, connection 2205, output device 2235, etc., to carry out the function.

[0159] Fig. 23 shows the step of an example method 2300 of neurofeedback training in a neurofeedback training session 200. In step 2302, an EEG signal may be obtained from electrodes worn on the user’s head during the neurofeedback training period 206, the EEG signal including, amongst others, alpha waves and beta waves. In step 2304, a relative power may be determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. In step 2306, one or more characteristics of the relative power over time may be determined during the neurofeedback training period 206. In step 2308, one or more parameters may be set of the neurofeedback sensory signal during the neurofeedback training period 206 or for a next neurofeedback training period in the neurofeedback training session 200 based on the one or more characteristics.

[0160] Fig. 24 shows the step of an example method 2400 of cognitive training in a neurofeedback training session 200, the cognitive training including one or more cognitive tasks 204, 208, 210, 212 to be performed by a user on a computer device. In step 2402, an EEG signal may be obtained from electrodes worn on the user’s head during the cognitive task 204, 208, 210, 212, the EEG signal including, amongst others, alpha waves and beta waves. In step 2404, a relative power may be determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. In step 2406, one or more characteristics of the relative power over time may be determined during the cognitive task 204, 208, 210, 212. In step 2408, one or more parameters may be set of the cognitive task 204, 208, 210, 212 or for a next cognitive task 204, 208, 210, 212 in the neurofeedback training session 200 based on the one or more characteristics.

[0161] Fig. 25 shows the step of an example method 2500 of optimizing a neurofeedback training session 200. The neurofeedback training session 200 may include an alternating sequence of neurofeedback training periods 206 and cognitive tasks 204, 208, 210, 212 to be performed by a user on a computer device. In step 2502, an EEG signal may be obtained from electrodes worn on the user’s head during the neurofeedback training periods 206 and during the cognitive tasks 204, 208, 210, 212, the EEG signal including, amongst others, alpha waves and beta waves. In step 2504, a relative power mat be determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. In step 2506, one or more characteristics of the relative power over time may be determined during the neurofeedback training periods 206 and during the cognitive tasks 204, 208, 210, 212. In step 2508, a duration may be set of the neurofeedback training periods 206 and / or the cognitive tasks 204, 208, 210, 212 based on the one or more characteristics.

[0162] In an embodiment, the EEG signal may be supplemented with one or more additional bio-signals, such as heart rate variability (HRV), electrodermal activity (EDA), respiration rate, and / or blood oxygen levels, which may be used to further refine the one or more characteristics.

[0163] In an embodiment, the one or more characteristics may be determined using a machine learning model trained on historic EEG data from the user and / or a population of users.

[0164] According to a further aspect of the present disclosure, a method of cognitive training in a neurofeedback training session is presented. The cognitive training includes one or more cognitive tasks to be performed by a user on a computer device. The method may include obtaining an EEG signal from electrodes worn on the user’s head during the cognitive task. The EEG signal includes brain waves including, amongst others, alpha waves and beta waves. The method may further include determining a relative power based on at least one of an alpha power of the alpha waves and a beta power of the beta waves. The method may further include determining one or more characteristics of the relative power over time during the cognitive task. The method may further include setting one or more parameters of the cognitive task or for a next cognitive task in the neurofeedback training session based on the one or more characteristics.

[0165] In an embodiment, the method of cognitive training in a neurofeedback training session may further include determining a change in slope in the relative power and determining a transition point where the change in slope in the relative power follows an abort pattern. The one or more characteristics may be based on the determined transition point.

[0166] In an embodiment, the method of cognitive training in a neurofeedback training session may include a cognitive training for resilience to stress, wherein the method may include determining whether or not a beta power based relative power is maintained at a substantially constant level between cognitive tasks. The one or more characteristics may be based on whether or not the beta power based relative power is maintained at the substantially constant level between the cognitive tasks.

[0167] In an embodiment, the method of cognitive training in a neurofeedback training session may include setting the one or more parameters such that a stressor of the cognitive task becomes larger to maintain the substantially constant level of the beta power based relative power between the cognitive tasks.

[0168] In an embodiment, the determining whether or not the beta power based relative power is maintained at a substantially constant level between the cognitive tasks may be based on an analysis of peak levels in the beta power based relative power, an analysis of an average beta power based relative power, and / or an analysis of trend in the beta power based relative power.

[0169] In an embodiment, the method of cognitive training in a neurofeedback training session may include a cognitive training for resilience to stress, wherein the method may include determining whether or not the relative power is maintained at a substantially low level between cognitive tasks. The one or more characteristics may be based on whether or not the relative power is maintained at the substantially low level between cognitive tasks.

[0170] In an embodiment, the method of cognitive training in a neurofeedback training session may include setting the one or more parameters such that a stressor of the cognitive task becomes larger to maintain the substantially low level of the relative power between the cognitive tasks.

[0171] In an embodiment, the determining whether or not the relative power is maintained at the substantially low level between cognitive tasks may be based on an analysis of dips in the relative power, an analysis of peaks on the relative power, and / or an analysis of an average relative power.

[0172] In an embodiment, the method of cognitive training in a neurofeedback training session may include a cognitive training for staying in a flow, wherein the method may include determining whether or not an alpha power based relative power is maintained at a substantially high level between cognitive tasks.

[0173] In an embodiment, the method of cognitive training in a neurofeedback training session may include determining a change in alpha power based relative power for subsequent cognitive tasks. The method may further include comparing the change in alpha power based relative power of the subsequent cognitive tasks.

[0174] In an embodiment, the comparing of the change in alpha power based relative power of the subsequent cognitive tasks may be based on an analysis of histogram data of the change in alpha power based relative power.

[0175] In an embodiment, the cognitive training may include a cognitive task for resilience to stress, a cognitive task for staying in a flow, a cognitive task for sustained attention, and / or a cognitive task for focus. A plurality of cognitive tasks may be performed alternating with a neurofeedback training period wherein a neurofeedback sensory signal is provided to the user.

[0176] In an embodiment, the relative power may be determined by calculating a ratio of alpha power over beta power. Alternatively, the relative power may be determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power.

[0177] In an embodiment, the relative power may be determined by calculating a ratio of beta power over alpha power. Alternatively, the relative power may be determined by calculating a ratio of beta power over a total brainwave power or over another brain wave power.

[0178] According to a further aspect of the present disclosure, a neurofeedback system is presented. The neurofeedback system may include a processing system configured for capturing an EEG signal from electrodes worn on a user’s head. The neurofeedback system may further include a neurofeedback playback device for presenting a neurofeedback sensory signal to the user. The processing system may include a processing module configured for adjusting one or more parameters of a cognitive task. The processing system may be configured to perform the method of cognitive training in a neurofeedback training session as described above.

[0179] In an embodiment, the neurofeedback system may further include a wearable device including an EEG sensor comprising the electrodes for capturing the EEG signal. The wearable device may be wirelessly communicatively connected to the processing system.

[0180] In an embodiment, the neurofeedback system may further include an intermediary device configured to relay the EEG signal from the wearable device to the processing system.

[0181] In an embodiment, the intermediary device may be a smartphone.

[0182] According to a further aspect of the present disclosure, a computer program is presented. The computer program may include instruction which, when the program is executed by one or more processors, cause the one or more processors to carry out the method of cognitive training in a neurofeedback training session as described above.

[0183] According to a further aspect of the present disclosure, a computer-readable storage medium is presented. The computer-readable storage medium may include instructions which, when executed by one or more processors, cause the one or more processors to carry out the method of cognitive training in a neurofeedback training session as described above.

[0184] According to a further aspect of the present disclosure, a method of neurofeedback training in a neurofeedback training session is presented. The neurofeedback training includes providing a neurofeedback sensory signal to a user during a neurofeedback training period. The method may include obtaining an EEG signal from electrodes worn on the user’s head during the neurofeedback training period. The EEG signal includes brain waves including, amongst others, alpha waves and beta waves. The method may further include determining a relative power based on at least one of an alpha power of the alpha waves and a beta power of the beta waves. The method may further include determining one or more characteristics of the relative power over time during the neurofeedback training period. The method may further include setting one or more parameters of the neurofeedback sensory signal during the neurofeedback training period or for a next neurofeedback training period in the neurofeedback training session based on the one or more characteristics.

[0185] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a rise period in the neurofeedback training period based on the relative power. The method may further include determining a decrease in rise of the relative power during the rise period. The one or more characteristics may be based on the determined decrease in rise of the relative power during the rise period.

[0186] In an embodiment, the rise period or rise time may be based on a starting value of the relative power, and / or the rise slope in an alpha power based relative power.

[0187] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a rise time tipping point from where an increase in rise of the relative power during the rise period changes into the decrease in rise of the relative power during the rise period. The one or more characteristics may be based on the determined rise time tipping point during the rise period.

[0188] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a further decrease in rise of the relative power during the rise period. The further decrease in rise of the relative power may be characterized by the relative power reaching a plateau level. The one or more characteristics may be based on the determined further decrease in rise of the relative power during the rise period.

[0189] In an embodiment, determining the rise period may further be based on contextual data comprising prior history data and / or a session context.

[0190] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining an end time of the rise period. The one or more characteristics may be based on the determined end time of the rise period. The one or more parameters may include a length of the neurofeedback training period or of the next neurofeedback training period in the neurofeedback training session. In case of neurofeedback training for resilience to stress, the length may be set to N x the end time with 0<A / <1 . In case of neurofeedback training for staying in a flow, the length may be set to N x the end time with N>1 .

[0191] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a plateau period in the neurofeedback training period based on the relative power The method may further include determining a level of the relative power during the plateau period. The one or more characteristics may be based on the determined level of the relative power during the plateau period. In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining one or more of a sustainable relative power level and a duration of the plateau period. The one or more characteristics may be based on the determined sustainable relative power level during the plateau period and / or the duration of the plateau period.

[0192] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a trend in the relative power level during the plateau period based on a duration and / or a strength of the relative power. The one or more characteristics may be based on the determined trend in the relative power level during the plateau period.

[0193] In an embodiment, determining the plateau period may be based on a spread in the relative power level around a median value in the EEG signal.

[0194] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include filtering the EEG signal based on histogram data having a normal distribution around the median value. Values outside a predefined percentiles range may be discarded. In an example, only values between the 15% and 85% percentiles may be accepted.

[0195] In an embodiment, the method of neurofeedback training in a neurofeedback training session may further include determining a fall period in the neurofeedback training period based on the relative power. The one or more characteristics may be based on the determined fall period.

[0196] In an embodiment, the fall period may be determined based on a starting value of the relative power, a fall time in the relative power, a fall slope in the relative power, and / or a given neurofeedback training period end time.

[0197] In an embodiment, a start of the fall period may be determined based on a median of histogram data of the relative power wherein the median starts to decrease.

[0198] In an embodiment, the relative power may be determined by calculating a ratio of alpha power over beta power. Alternatively, the relative power may be determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power.

[0199] In an embodiment, the relative power may be determined by calculating a ratio of beta power over alpha power. Alternatively, the relative power may be determined by calculating a ratio of beta power over a total brainwave power or over another brain wave power.

[0200] In an embodiment, the neurofeedback sensory signal comprises sounds presented to the user, music presented to the user, light presented to the user, a temperature presented to the user, a smell presented to the user, and / or haptics feedback presented to the user.

[0201] In an embodiment, the neurofeedback sensory signal includes music, wherein the music may be adapted in real time using the one or more parameters. The parameters are, e.g., used for changing audio frequencies, tempo and / or momentum.

[0202] According to a further aspect of the present disclosure a neurofeedback system is presented. The neurofeedback system may include a processing system configured for capturing an EEG signal from electrodes worn on a user’s head. The neurofeedback system may further include a neurofeedback playback device for presenting a neurofeedback sensory signal to the user. The processing system may include a processing module configured for adjusting one or more parameters of the neurofeedback sensory signal relative to a default setting of the neurofeedback sensory signal. The processing system may further include a signal interface configured for outputting the adjusted neurofeedback sensory signal to the neurofeedback playback device. The processing system may be configured to perform the method of neurofeedback training in a neurofeedback training session as described above.

[0203] In an embodiment, the neurofeedback system may further include a wearable device including an EEG sensor comprising the electrodes for capturing the EEG signal. The wearable device may be wirelessly communicatively connected to the processing system.

[0204] In an embodiment, the neurofeedback system may further include an intermediary device configured to relay the EEG signal from the wearable device to the processing system.

[0205] In an embodiment, the intermediary device may be a smartphone.

[0206] According to a further aspect of the present disclosure, a computer program is presented. The computer program may include instruction which, when the program is executed by one or more processors, cause the one or more processors to carry out the method of neurofeedback training in a neurofeedback training session as described above.

[0207] According to a further aspect of the present disclosure, a computer-readable storage medium is presented. The computer-readable storage medium may include instructions which, when executed by one or more processors, cause the one or more processors to carry out the method of neurofeedback training in a neurofeedback training session as described above.

[0208] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope thereof.

Claims

1. CLAIMS1. A method (2500) of optimizing a neurofeedback training session (200), the neurofeedback training session (200) comprising an alternating sequence of neurofeedback training periods (206) and cognitive tasks (204, 208, 210, 212) to be performed by a user on a computer device, the method comprising: obtaining (2502) an electroencephalogram, EEG, signal from electrodes worn on the user’s head during the neurofeedback training periods (206) and during the cognitive tasks (204, 208, 210, 212), the EEG signal comprising alpha waves and beta waves; determining (2504) a relative power based on at least an alpha power of the alpha waves and a beta power of the beta waves; determining (2506) one or more characteristics of the relative power over time during the neurofeedback training periods (206) and during the cognitive tasks (204, 208, 210, 212); and setting (2508) a duration of the neurofeedback training periods (206) and / or the cognitive tasks (204, 208, 210, 212) based on the one or more characteristics.

2. The method according to claim 1 , further comprising: adapting the sequence of the neurofeedback training periods (206) and the cognitive tasks (204, 208, 210, 212) based on the one or more characteristics.

3. The method according to any one of the preceding claims, comprising: determining a termination time for a neurofeedback training period (206) based on the one or more characteristics.

4. The method according to any one of the preceding claims, comprising: determining a termination time for a cognitive tasks (204, 208, 210, 212, 1002,1102, 1104, 1106, 1202, 1302, 1304, 1306, 1308, 1402) based on the one or more characteristics.

5. The method according to any one of the preceding claims, comprising:determining one or more parameters of a neurofeedback sensory signal for use during a neurofeedback training period (206) based on the one or more characteristics.

6. The method according to any one of the preceding claims, comprising: determining one or more parameters of a cognitive task (204, 208, 210, 212,1002, 1102, 1104, 1106, 1202, 1302, 1304, 1306, 1308, 1402) based on the one or more characteristics.

7. The method according to any one of the preceding claims, comprising: determining a neurofeedback training period (206) or a cognitive tasks (204, 208,210, 212) to be performed after a current neurofeedback training period (206) or a current cognitive task (204, 208, 210, 212) based on the one or more characteristics.

8. The method according to any one of the preceding claims, comprising: wherein the one or more characteristics are based on a duration of a neurofeedback training period (206) when maximizing the impact of a resilience to stress training during the neurofeedback training periods (206), the method comprising comparing the duration of a plurality of the neurofeedback training periods (206), and the one or more parameters set the duration of a next neurofeedback training period (206).

9. The method according to any one of the claims 1-8, comprising: a training for resilience to stress, wherein determining a plateau rise time (Tp) in the relative power during a neurofeedback training period (206) for training for resilience to stress, and wherein the duration of the neurofeedback training period (206) for training for resilience to stress is based on the determined plateau rise time (Tp).

10. The method according to claim 9, wherein a transition time for terminating the neurofeedback training period (206) for training for resilience to stress and transitioning to a cognitive task is determinedbased on the plateau rise time (Tp) and a predefined period (X) for maintaining a plateau level in the relative power.

11. The method according to claim 10, wherein the duration of the neurofeedback training period (206) for training for resilience to stress is calculated as (1+X) x Tp, with X being the predefined period and TP being the plateau rise time.

12. The method according to claim 11 , wherein, for training for resilience to stress, 0<X<1.

13. The method according to any one of the claims 1-8, comprising: a training for staying in a flow, wherein determining a plateau rise time (Tp) in the relative power during a neurofeedback training period (206) for training for staying in a flow, and wherein the duration of the neurofeedback training period (206) for training for staying in a flow is based on the determined plateau rise time (Tp).

14. The method according to claim 13, wherein a transition time for terminating the neurofeedback training period (206) for training for staying in a flow and transition to a cognitive task is determined based on the plateau rise time (Tp) and a predefined period (X) for maintaining a plateau level in the relative power.

15. The method according to claim 14, wherein the duration of the neurofeedback training period (206) for training for staying in a flow is calculated as (1 +X) x Tp, with X being the predefined period and TP being the plateau rise time.

16. The method according to claim 15, wherein, for training for staying in a flow, X>1 .

17. The method according to any one of the claims 9-16, further comprising: determining a start time (Ts) in the relative power of the neurofeedback training period (206),and wherein the duration of the neurofeedback training period (206) is based on the determined start time (Ts).

18. The method according to any one of the claims 9-17, further comprising: determining a delta time (TA) in the relative power of the neurofeedback training period (206) where a rise time (T) in the relative power starts, and wherein the duration of the neurofeedback training period (206) is based on the determined delta time (TA).

19. The method according to any one of the claims 9-18, further comprising: determining a fall time (TF) in the relative power where the relative power ratio starts to drop, and wherein the duration of the neurofeedback training period (206) is based on the determined fall time (TF).

20. The method according to any one of the claims 1-8, comprising: a training for resilience to stress, wherein determining a plateau rise time (TSP) in a beta power based relative power during a cognitive task (204, 208, 210, 212) for training for resilience to stress, and wherein the duration of the cognitive task (204, 208, 210, 212) for training for resilience to stress is based on the determined plateau rise time (TSP).21 . The method according to claim 20, wherein a transition time for terminating the cognitive task (204, 208, 210, 212) for training for resilience to stress and transitioning to a neurofeedback training period (206) is determined based on the plateau rise time (TSP) and a predefined period (Y) for maintaining a plateau level in the beta power based relative power.

22. The method according to claim 21 , wherein the duration of the cognitive task (204, 208, 210, 212) for training for resilience to stress is calculated as (1+Y) x TSP, with Y being the predefined period and TSP being the plateau rise time.

23. The method according to claim 22, wherein, for training for resilience to stress, 0<Y<1.

24. The method according to any one of the claims 20-23, further comprising: determining a fall time (TSF) in the relative power where the relative power ratio starts to drop, and wherein the duration of the cognitive task (204, 208, 210, 212) is based on the determined fall time (TSF).

25. The method according to any one of the claims 1-24, wherein the relative power is determined by calculating a ratio of alpha power over beta power, a / p, or wherein the relative power is determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power.

26. The method according to any one of the claims 1-24, wherein the relative power is determined by calculating a ratio of beta power over alpha power, p / a, or wherein the relative power is determined by calculating a ratio of alpha power over a total brainwave power or over another brain wave power.

27. A neurofeedback system (2100) comprising: a processing system (2102) configured for capturing an electroencephalogram (EEG) signal from electrodes worn on a user’s head; and a neurofeedback playback device (2104) for presenting a neurofeedback sensory signal to the user during a neurofeedback training period (206), wherein the processing system (2102) comprises: a processing module configured for adjusting one or more parameters of the neurofeedback sensory signal relative to a default setting of the neurofeedback sensory signal, the processing module further configured for adjusting one or more parameters of a cognitive task (204, 208, 210, 212) to be performed by a user on a computer device; andand wherein the processing system (2102) is configured to perform the method according to any one of the claims 1-26.

28. The neurofeedback system (2100) according to claim 27, further comprising: a wearable device (2108) including an EEG sensor comprising the electrodes for capturing the EEG signal, wherein the wearable device (2108) is wirelessly communicatively connected to the processing system (2100).

29. The neurofeedback system (2100) according to claim 28, further comprising: an intermediary device (2110) configured to relay the EEG signal from the wearable device (2108) to the processing system (2102), the intermediary device preferably being a smartphone.

30. A computer program comprising instruction which, when the program is executed by one or more processors, cause the one or more processors to carry out the method according to any one of the claims 1-26.

31. A computer-readable storage medium comprising instructions which, when executed by one or more processors, cause the one or more processors to carry out the method according to any one of the claims 1-26.

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