Activation of a direct neural interface

Abstract

The invention relates to a method for activating a direct neural interface (BCI), available to a given user (UT) in order to control at least one machine, the method including: - activating (BCI-i ON) a direct neural interface (BCI-i) associated with a mental state detected according to an analysis of a brain signal (SIG.CER.) of the user by a passive direct neural interface in order to control at least the machine via the direct neural interface.

Description

Activation of a direct neural interface

[0001] This disclosure falls within the domain of direct neural interfaces (or BCIs hereafter, for "Brain Computer Interface").

[0002] The principle of such BCI interfaces, known as "reactive", is based on: - the emission of a stimulus (auditory, visual, or tactile) to a user, this stimulus being emitted by a transducer powered by a signal with a modulation frequency chosen for example between 1 and 200Hz, and - in return to the perception of this stimulus by the user, the detection of evoked potentials in an electroencephalogram signal (called "EEG signal" below) in which the aforementioned modulation frequency is found.

[0003] Thus, in the context of a reactive BCI interface, the term "evoked potential" refers to a signal that appears in electroencephalogram (EEG) signals when a user is exposed to a stimulus. For example, in the SSAEP (Steady-State Auditory Evoked Potential) approach, the stimulus consists of a signal, such as an auditory or visual signal, with a carrier frequency to which an amplitude modulation is applied. This modulation can be sinusoidal or square wave (in the form of regular "beeps"). Accordingly, equipment such as a headset with EEG sensors allows for the measurement of EEG signals and the identification of the modulation frequency within these signals.

[0004] An example of a stimulus used for an SSAEP application might be a sinusoidal function at a frequency audible to everyone (e.g., 20 Hz to 20 kHz), and amplitude-modulated at another frequency, for example, between 1 and 200 Hz. The high-frequency sine wave acts as the carrier wave and allows a user to simply hear the "beeps" of the low-frequency modulating signal. It is the modulating signal that constitutes the information of interest, as it is the modulation frequency that is found in the evoked potential.

[0005] Indeed, it is possible to detect stimulation frequencies between approximately 1 and 200 Hz in dedicated areas of the brain, particularly auditory and visual areas. However, at this frequency range of 1 to 200 Hz, the frequencies are low and can be difficult to reproduce and / or hear. This is why a carrier frequency is used as a vector, allowing the user to hear these frequencies more easily. A stimulus (a low-frequency modulating sine wave of the type 1 to 200 Hz) can then generate a measurable evoked potential in the brain at the same low frequency of 1 to 200 Hz (plus any harmonics). This frequency is then measurable in the EEG signals of the person exposed to the stimulus. Therefore, the low-frequency modulation (1 to 200 Hz) can be detected in the user's EEG signals.

[0006] Therefore, it is possible to build an interface by directly searching for the interface signal in the user's brainwaves. The principle of such an interface, called a "BCI" (for "Brain-Computer Interface"), is outlined in Figure 1. A UT user wears a headset equipped with electroencephalogram (EEG) signal sensors; this headset can be part of a general BCI interface. Various stimuli (auditory, visual, or other), each with its own modulation frequency, are presented to the user, who then focuses on a single stimulus. The same modulation frequency as the stimulus on which the user focused can then be measured in their EEG signals.

[0007] In the example shown in Figure 1, several stimuli from respective sources HP1, HP2, and HP3, with different modulation frequencies and representing, for example, different instructions for operating a machine (e.g., "turn left," "turn right," "brake"), are presented simultaneously to a user. The user focuses on one of these stimuli, so that their EEG signals reveal the frequency of one of the HP1 stimuli, and the function associated with that stimulus is then executed by the machine. In Figure 2, the stimuli are delivered by respective speakers HP1, HP2, HP3, etc., and the user focuses on one of these sound sources. The stimulus is therefore auditory.

[0008] In the example of a stimulus delivered by a light source, the latter may blink at the aforementioned modulation frequency. An analysis of the user's EEG signals reveals the presence of a frequency corresponding to the blinking frequency. For example, the light source may have a particular color, thus corresponding to a wavelength LO associated with a carrier frequency fp by a relationship of the type LO = c / fp, where c is the speed of light. Some users may be more sensitive to certain colors than others to capture their attention. Thus, the choice of color (and therefore of the carrier frequency fp) can be important, particularly at certain times of day for the same user, to efficiently collect EEG signals revealing a usable evoked potential (i.e., having a frequency corresponding to the modulation or "blinking" frequency of the light source).For example, a wavelength of 0.5µm can be used for blue as the color of visual stimuli in some subjects, or a wavelength of 0.55µm for green to which some users are more sensitive, or even 0.65µm for red, a color to which other users are even more sensitive, etc.

[0009] Thus, a direct neural interface, or "DNI," can be used as a human-computer interface, ensuring direct communication between the brain and an external device, such as a computer. Through their brainwaves, a user can then remotely control a device. DNIs can be classified according to their mode of operation: they can be active, reactive, or passive.

[0010] An "active" BCI requires the user to voluntarily produce a brain signal to control an external device. The user voluntarily produces a brain signal by thinking and / or imagining an object, an action, or something else, using at least one of their senses, i.e., by visualizing a word, a shape, a gesture, or something else, or by imagining the sensation of a manipulation or listening, for example, by imagining a movement.

[0011] A so-called "reactive" BCI detects, in the brain, a signal triggered by an external stimulus, for example a flashing light or a sound with particular characteristics, and uses it to control a device, as described above with reference to the.

[0012] A so-called "passive" BCI analyzes the user's spontaneous brain activity, without external stimulus or particular intention, which makes it possible to obtain data on their mental state (stress, concentration, fatigue, etc.).

[0013] When a user wants to activate / initiate a reactive BCI, of the type described above, they are forced to use another method, such as a computer mouse or keyboard, or a physical button. This can be complicated if the user's hands are unavailable (occupied with another task, for example) or if the user has reduced mobility.

[0014] Furthermore, the stimuli emitted by a responsive IBC can be stressful for the user, who may then experience fatigue or even irritation from these stimuli if they do not need to use the IBC regularly. Moreover, the emission of such stimuli consumes energy. By focusing on a stimulus to turn off / deactivate the IBC interface, the emission of these stimuli can cease. However, if the user then wants to use the IBC again, there is no provision other than a physical means (push button or computer mouse) to reactivate it, which poses a further problem if the user's hands are unavailable or if the user has reduced mobility. Summary

[0015] This disclosure improves the situation.

[0016] A method is proposed for activating a direct neural interface, available to a given user to control at least one machine. The method comprises: activating a direct neural interface associated with a mental state detected based on an analysis of a brain signal from the user by a passive direct neural interface to control at least said machine via the direct neural interface. The passive direct neural interface is also called the first direct neural interface, and the direct neural interface activated by the method according to the invention is called the second direct neural interface.

[0017] This solution proposes to activate, or initiate, a BCI via the detection of a specific brain signal without requiring any other modality (physical button, computer mouse, or other). Indeed, the first direct neural interface, which is passive in this case, activates the second direct neural interface using the activation method according to the invention.

[0018] In one embodiment, the process may include: - detecting, based on the analysis of the brain signal measured by a passive direct neural interface, whether the user is in a predefined mental state, the direct neural interface being activated in response to this detection of said predefined mental state.

[0019] It is recalled that a "passive" interface can detect a particular mental situation such as a situation of stress, or of awakening after drowsiness, or others, and that an "active" interface makes it possible to detect that the user is thinking about moving a limb (for example raising the left arm) and this thought can then cause the activation of an active and / or reactive BCI for example.

[0020] The user's "mental situation" refers to both a situation where the user imagines moving a limb (the left arm, or the right leg), and a mental situation linked to a current state of the user (for example, stress, wakefulness, or others).

[0021] In a design, the process may include: - analyzing said brain signal of the user.

[0022] Such a sensor can be placed near an appropriate region of the brain to measure the signal specific to the aforementioned mental state.

[0023] For example, the detection of this specific brain signal can be achieved using a different brain-computer interface (BCI) than the one used to control the aforementioned machine. This could involve, for instance, activating / initiating a reactive BCI of the type described above, based on a detection performed by an active BCI (resulting from the user's thought of a gesture, an object, or other) or by a passive BCI (resulting from the user's experience of stress, awakening from drowsiness, or other specific mental states). Alternatively, it could involve activating / initiating an active BCI (controlling a machine through the user's thought of a gesture, an object, or other) based on a detection performed by an active BCI or a passive BCI (resulting from the user's experience of stress, awakening from drowsiness, or other).Thus, the same BCI interface, of the "active" type in this case, can both detect the specific brain signal, related to the user's current mental state, and control the aforementioned machine (by the user's thought of a gesture, an object or other).

[0024] Thus, in a given implementation, the direct neural interface associated with said mental situation to control the machine can be one of the following direct neural interfaces: - a reactive direct neural interface; and - an active direct neural interface.

[0025] The term "direct reactive neural interface" refers to an interface that can be visual, auditory, or otherwise, emitting stimuli modulated at respective frequencies, as presented above with reference to the.

[0026] An "active direct neural interface" is defined as an interface that allows the user to control a machine with their thoughts. For example, thinking about raising your left arm increases the volume of a device, while thinking about raising your right arm decreases it.

[0027] In a realization, the analysis of the brain signal may include a comparison of the measured brain signal to at least one predefined signal corresponding to said predefined mental state.

[0028] This could involve, for example, comparing the time and / or frequency profile of the measured signal to a typical profile associated with this predefined situation.

[0029] In a realization, the analysis of the brain signal may include, more particularly, a comparison of the measured brain signal to a plurality of predefined signals, each corresponding to: - a predefined mental state of the user, and - a direct neural interface to be activated.

[0030] Thus, depending on the mental state detected via the analysis of the measured signal, it is possible to activate a specific BCI interface corresponding to the detected mental state (for example, detected stress can generate a BCI interface requesting assistance).

[0031] In one implementation, data from predefined signals are stored in a database corresponding to respective identifiers of direct neural interfaces to be activated.

[0032] Thus, in such a realization, it is possible to compare in parallel or in turn (with an order of priority for example for emergency situations) the base signals with the measured brain signal and to activate the BCI interface corresponding to the base signal closest to the measured signal for example.

[0033] In one implementation, the process may include learning a predefined temporal signal profile, at least for the aforementioned user, and (in a typical inference phase) searching for this temporal profile in the brain signal measured on the user.

[0034] The aforementioned learning can typically be achieved by training an artificial intelligence, for example on the user (at different times of a day, for example) or alternatively on a group of users.

[0035] In another aspect, a computer program is proposed that includes instructions for implementing all or part of a process as defined herein when executed by a processor. In another aspect, a non-transient, computer-readable recording medium is proposed on which such a program is recorded.

[0036] According to another aspect, a computer device is proposed, for activating a direct neural interface, available to a given user to control at least one machine, the device including a processing circuit for implementing the above process.

[0037] According to yet another aspect, it is proposed a direct neural interface equipment, available to a given user to control at least one machine, comprising an activator of the direct neural interface associated with a mental situation detected according to a result provided by an analyzer of a brain signal of the user by a passive direct neural interface to control said at least one machine via said direct neural interface.

[0038] In a given implementation, the activator of this equipment can be a computer device as defined above.

[0039] In a design, the equipment may include: - one or more direct neural interface sensors, and - one or more sensors for measuring said brain signal, in the same headset intended to be worn by the user.

[0040] In an implementation where the direct neural interface is reactive and visual stimuli, the equipment may then include a computer screen configured to display respective command icons of the aforementioned machine, these icons flashing at respective frequencies, and a screen page containing these icons may then be displayed on the screen, upon activation of said direct neural interface (i.e. upon detection of the brain signal corresponding to the predefined mental situation).

[0041] In one implementation, the equipment may include a brain signal analyzer configured to compare the measured brain signal to a plurality of predefined signals, each corresponding to a direct neural interface to be activated. Each of these direct neural interfaces is reactive and responds to visual stimuli. Thus, the computer screen can then be configured to display a screen page specific to a particular direct neural interface to be activated (following the detection of the mental state associated with that screen page).

[0042] Such a screen could be, for example, a computer tablet capable of displaying a page selected from several possible screen pages of different BCI interfaces, depending on the detected mental state.

[0043] Other features, details, and advantages will become apparent upon reading the detailed description below and analyzing the attached drawings, which, in addition to those mentioned above: Fig. 2

[0044] shows examples of brain signals that can be detected by one or more sensors, for example from a passive BCI interface according to one embodiment. Fig. 3

[0045] shows the steps of a process of the type described above, according to an example of implementation. Fig. 4

[0046] schematically shows a device for implementing the above process, according to an example of implementation.

[0047] The proposed solution consists of activating, or initiating, an active and / or reactive BCI through detection by a passive BCI of the type described previously with reference to the.

[0048] Detection by a passive BCI, for example, can be achieved through metrics presented by the brain signals of users subjected to situations such as a given level of concentration, or stress, fatigue, or a state of drowsiness or wakefulness.

[0049] A passive brainwave encoder (BCI) typically detects when a person wakes up, automatically initiating the possibility for a user to interact with a machine via an active or reactive BCI. A passive BCI interface may include one or more sensors that listen to a user's brainwaves to detect a specific signal and, upon detection, activate an active or reactive BCI.

[0050] The listening by the sensors of the user's brain can be permanent, or carried out in a recurring or repetitive manner, for example during a predefined time range or duration at a predefined time interval or period, or punctual (for example triggered by a geographical position of the user or a change in the user's physical position...).

[0051] In another possible embodiment, distinctive "Error-Related Negativity" signals can appear in a user's EEG signals when they become aware that they have made an error. The detection of such a signal can trigger the activation of an active or reactive BCI. For example, if the user routinely performs a task and makes an error, a BCI can be activated to allow them to correct it. Thus, a technician equipped with a mixed BCI as described herein might injure themselves, rendering them unable to interact with their arms. This situation generates a measurable Error-Related Negativity signal in their brain.The mixed BCI interface detects this signal and allows it, for example, to initiate an emergency communication via an active BCI (imagine a gesture that triggers a call) or a reactive BCI (the detection of the aforementioned signal triggering the generation of stimuli necessary for the implementation of the reactive BCI).

[0052] In a first embodiment, the CAP sensor is a passive direct neural interface sensor. In a second embodiment, a mixed neural interface comprises the CAP sensor, the passive direct neural interface, and the neural interface activated by the activation method according to the invention, the CAP sensor being connected to both direct neural interfaces of the mixed neural interface. In this second embodiment, the CAP sensor is, in particular, capable of providing a brain signal to the passive direct neural interface as long as the neural interface activated by the activation method according to the invention is not active, and to the neural interface activated by the activation method according to the invention as soon as the latter is activated.

[0053] With reference to the model, a first type of signal S1(t) that can be detected could be, for example, a transition from drowsiness to wakefulness. To this end, one or more sensors judiciously positioned near relevant brain regions can measure brain waves for a single user, or for a group of users, who are in such a state of transition from drowsiness to wakefulness. From this, an average temporal profile of this brain signal S1(t) can be deduced; this average profile is denoted Sm on the model.

[0054] Data from this average profile Sm can be stored in memory, with chosen standard deviations of amplitudes (one, two, or three times sigma, depending on the noise level of the brain signal being measured). These standard deviation curves S+ and S- are represented by dashed lines on the graph. Thus, typically, a brain signal SIG.CER. commonly measured on a given user can be compared to this profile Sm and the corresponding standard deviations S+ and S- to determine if that user is currently in a state of transition from sleep to wakefulness. For example, the measured SIG.CER. signal can be smoothed, and it can be verified that this smoothed signal does not fall outside the defined boundaries between S+ and S-. As a complement or alternative, a Fourier transform (e.g., a time-frequency transform) can also be performed to analyze the frequencies of these brain signals.

[0055] More generally, as an alternative or complement, artificial intelligence can learn the signal patterns measured during a human user's transition from a state of drowsiness to a state of wakefulness, and it can then be determined, through inference by this artificial intelligence, whether the measured brain signal SIG.CER. corresponds to a given user's transition from drowsiness to wakefulness.

[0056] At the top of the image, a brain signal S1(t) is shown, which could correspond to the transition from a state of drowsiness to a state of wakefulness. At the bottom of the image, another type of brain signal, S2(t), is shown, which could, for example, correspond to the onset of a stressful situation for a user. For both types of signals, it is possible, based on learned temporal profiles, to determine a typical state for a given user. Thus, it is possible to distinguish several types of brain signals corresponding to different states of a user (wakefulness, stress, or others).

[0057] For example, sensors in a passive neural interface are thus able to measure and discriminate such brain signals, so as to then activate direct neural interfaces BCIs (active and / or reactive) each corresponding to a type of measured brain signal, which then allows a machine corresponding to this measured brain signal to be controlled via an appropriate BCI interface.

[0058] Therefore, it is possible to create a database of typical brain signals (S1, S2, ..., Si) corresponding to distinct user situations (awakeness, stress, imagining raising the right arm, etc.) and their respective identifiers of brain-computer interfaces (BCIs) (active or reactive) to be activated and used to control distinct devices (for example, a television, a telephone, or a distress signal transmitter, etc.). These typical brain signals can then be of different types: for example, a signal from drowsiness to wakefulness to activate a home automation BCI (controlling electric blinds, lights, etc.), or a stress signal to activate a BCI controlling a distress signal transmitter, etc.

[0059] We have illustrated, as an example, a process using such a BDD database. A first step, E1, consists of acquiring brain signals from a given user using continuously active sensors. Each acquired signal is compared, in step E2, to the signals in the BDD database, which was created (for example, by training an artificial intelligence) in a previous step, E0. If, typically in step E3, a stressful situation for the user has been detected as corresponding to the brain signal S2, in step E4, a BCI-1 interface can be activated to control a machine in response to a request for assistance, for example.Otherwise (KO arrow at the end of test E3), the measured signal, compared to other signals in the database, may correspond, for example, to that characterizing a state of wakefulness after drowsiness. In this case, if a match is detected in step E5 with this signal S1 (OK arrow at the end of test E5), a corresponding BCI-2 interface can be activated in step E6 to control a device such as a radio or television (turning it on, increasing the volume, changing the station or channel, turning it off, etc.). The process can continue with a plurality of signals from the database tested successively. If a match is detected with one of these signals at a general step E7, a corresponding BCI-1 interface can be activated in step E8.The process can continuously loop back to the first step E1 of measuring the user's brain signal UT, possibly to activate a new BCI interface, according to a user state commonly detected in step E2 and in one of the steps E3, E5, E7.

[0060] For each of these BCI-i interfaces, "buttons" (for example, computer icons) can flash at different frequencies on a control panel, for instance. Thus, in a given implementation, it is possible to use, for example, the same computer screen, such as the screen of a tablet computer (reference TAB described later), to display the icons associated with a BCI interface on the same screen (and have these icons flash at their specific frequencies) when that BCI interface is activated. Then, if another type of brain signal is detected, the tablet's screen is simply updated with new flashing icons to activate another BCI interface following this new detection. Of course, between two detections of a specific brain signal, the tablet can be turned off and only turn on upon detection of one of these specific brain signals.

[0061] A single BCI interface can control different types of machines. It can therefore be understood that, broadly speaking, functions specific to different user situations (awakeness, stress, etc.) can be grouped into the same BCI interface "page" to control one or more machines depending on the user's current situation.

[0062] Furthermore, with reference to the previous point, the equipment available to the user may advantageously include a single headset equipped with at least one CAP sensor to detect the brain signal triggering the activation of a BCI interface, as well as one or (in practice) several sensors to use this BCI. The CAP sensor(s) for detecting the aforementioned brain signal can be positioned near a specific brain region to effectively detect this signal. For example, stress can be detected by measuring a CAP sensor positioned near the prefrontal cortex. Error-Related Negativity, on the other hand, can be detected near the dorsal anterior cingulate cortex, as well as near the motor cortex. It is therefore understandable that several signals can be detected by sensors positioned differently.Thus, the detection of the same mental state can be based on the analysis of several brain signals, notably measured by different sensors and / or positioned differently.

[0063] The other sensor(s) for using the BCI to control a machine are located near the area of ​​the brain usually used for the correct use of a BCI (e.g., the auditory areas of the brain for a reactive BCI based on sound stimuli, or the vision areas for visual signals).

[0064] All or part of the steps of the process can be implemented by a device as illustrated in the figure, this device comprising a processing circuit equipped with: - an input interface IN to receive data from one or more CAP sensors of brain signals SIG.CER., - a memory MEM storing in particular instruction data of a computer program for the implementation of the above process (and possibly for example data from the content of the database BDD), - a processor PROC capable of accessing the memory MEM to read and execute the instructions of the aforementioned computer program, for the implementation of the process, the processor performing an analysis of these signals to possibly detect a current situation of the user UT (stress, arousal, etc.), - and an output interface OUT capable of delivering in particular an activation signal of a BCI-i interface corresponding to the current situation of the user.In particular, the PROC processor also receives brain signal data acquired by the CAP sensors, via the IN input interface.

[0065] As previously mentioned, the BCI-i ON activation signal can be interpreted by an input interface of a tablet computer, for example, to control the display of a screen page on the tablet by flashing (at different frequencies) icons associated with BCI-i interface functions to be activated. The user focuses on one of these flashing icons, and the EEG signals (containing this flashing frequency) are detected by the BCI sensors of the equipment worn by the user and can then be analyzed to control the operation of one or more machines associated with that BCI-i interface.

[0066] These technical solutions can be applied to all existing or future BCIs, which usually require another method (push button, computer mouse, etc.) to be launched.

[0067] This disclosure is not limited to the forms of implementation described above, only by way of example, but encompasses all the variants that a person skilled in the art may consider within the framework of the protection sought.

[0068] Typically, the signals measured by a BCI interface (active, reactive or passive) can be EEG signals as described previously, but alternatively also and more generally any brain signals (including MRI (magnetic resonance imaging), or functional MRI or MEG, or others).

Claims

Method for activating a direct neural interface (DNI-BCI), available to a given user (UT) to control at least one machine, the method comprising: activating (E4; E6; … E8) a direct neural interface (DNI-BCI-1; DNI-BCI-2; … DNI-BCI-i) associated with a mental state detected based on an analysis of a brain signal of the user by a passive direct neural interface to control at least said machine via the direct neural interface. A method according to claim 1, wherein the method comprises: detecting (E3; E5; … E7), based on the analysis of the brain signal by the passive direct neural interface, whether the user (UT) is in a predefined mental state, the direct neural interface being activated (E4; E5;… E8) in response to this detection of said predefined mental state. A method according to any one of the preceding claims, wherein the method comprises: - analyzing (E2) said brain signal of the user. A method according to any one of the preceding claims, wherein the direct neural interface (BCI-1; BCI-2; … BCI-i) associated with said mental state for controlling the machine is a direct neural interface among the following: - a reactive direct neural interface; and - an active direct neural interface. A method according to any one of the preceding claims, wherein the brain signal analysis comprises a comparison (E2) of the measured brain signal (SIG.CER.) among the following comparisons: - a comparison (E2) of the measured brain signal (SIG.CER.) to at least one predefined signal (S1, S2, … Si) corresponding to said predefined mental state; and - a comparison (E2) of the measured brain signal (SIG.CER.) to a plurality of predefined signals (S1, S2, … Si) each corresponding: * to a predefined mental state of the user, and * to a direct neural interface (BCI-1; BCI-2; … BCI-i) to be activated. Method according to the preceding claim, wherein predefined signal data are stored in a database corresponding to respective identifiers of direct neural interfaces (BCI-1; BCI-2; … BCI-i) to be activated. A method according to any one of claims 5 or 6, comprising learning a predefined temporal signal profile (S1(t), S2(t)), at least for said user, and searching for said temporal profile in the brain signal measured on the user. Computer program comprising instructions for implementing the method according to one of the preceding claims, when executed by a processor. Computer device, for activating a direct neural interface (BCI-i), available to a given user (UT) to control at least one machine, the device comprising a processing circuit for implementing the method according to one of claims 1 to 7. Direct neural interface (DNI-I) equipment, available to a given user (UT) to control at least one machine, comprising a direct neural interface (DNI-I) activator associated with a mental state detected based on a result provided by a brain signal analyzer of the user by a passive direct neural interface to control said at least one machine via said direct neural interface. Equipment according to claim 11, wherein the activator is a computer device according to claim 9. Equipment according to one of claims 10 or 11, comprising: - one or more direct neural interface sensors (BCI), and - one or more brain signal measurement sensors (CAP) (SIG.CER.), in the same helmet intended to be worn by the user. Equipment according to any one of claims 10 to 12, wherein the direct neural interface (BCI-i) is reactive and to visual stimuli, and, the equipment comprising a computer screen (TAB), configured to display respective machine control icons, said icons flashing at respective frequencies, a screen page comprising said icons is displayed on the screen, upon activation of said direct neural interface. Equipment according to the preceding claim, wherein the equipment comprises a brain signal analyzer configured to compare (E2) the measured brain signal (SIG.CER.) to a plurality of predefined signals (S1, S2, … Si) each corresponding to a direct neural interface (BCI-1; BCI-2; … BCI-i) to be activated, each of the direct neural interfaces (BCI-1; BCI-2; … BCI-i) being reactive and to visual stimuli, and wherein the computer screen (TAB) is configured to display a screen page specific to a direct neural interface to be activated (BCI-1; BCI-2; … BCI-i).

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