Lighting control for brain-controlled interface systems

Abstract

A brain-controlled interface system is disclosed, the brain-controlled interface system including a brain-controlled interface configured to detect brain signals indicative of a user's brain activity in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a lighting controller configured to control the one or more lighting devices, and one or more processors configured to analyze the brain signals to identify a level of noise in the brain signals when the current light scene is active, and if the level of noise exceeds a threshold, adjust the light scene while monitoring the level of noise until a target level of noise in the brain signals is established.

Description

[Technical field]

[0001] The present invention relates to a brain control interface system.The present invention further relates to a method for adjusting a light scene, and to a computer program for carrying out said method. [Background technology]

[0002] Brainwave-based device control is an emerging technology. Brain-computer-interfaces (BCIs) are used to detect a user's brain signals and derive information from these brain signals. This information can indicate, for example, the user's thoughts or actions. Thoughts can indicate, for example, control commands for a controllable device, such as a lighting device. An example of such a system is disclosed in US10551921B2. There are two main types of BCIs: non-invasive and invasive. Non-invasive types are the most common and involve placing sensors (electrodes) on the human head. These measure brain activity and transfer the data to a computer. Most BCIs use electroencephalography (EEG) systems, where electrodes are typically attached to the scalp to measure electrical currents sent by neurons in the brain. Changes in this electrical current reflect brain activity. Because when an individual thinks about something or performs an action, hundreds of thousands of neurons fire. This generates an electrical current that is large enough to be measured at the scalp. A computer system then attempts to interpret this data to derive the user's actions or thoughts. Alternatives to EEG systems include electrooculography (EOG), electromyography (EMG), electrodermal activity (EDA), and photoplethysmography (PPG) systems. As an alternative to utilizing electrodes on the surface of the scalp, implantable brain-computer interfaces may be used. In this case, a probe is inserted into the brain through an automated process performed by a surgical robot.Each probe includes an area of ​​wires containing electrodes that can locate electrical signals in the brain, and a sensory area where the wires interact with an electronic system that allows amplification and acquisition of brain signals.

[0003] A research study (Min et al. - Bright Illumination Reduces parietal EEG alpha activity during a sustained attention task. Brain Research, 2013.) conducted several experiments of sustained attention on subjects under different lighting conditions. EEG was recorded from the parietal part of the brain. The study found that brain pulses were significantly affected by the illuminance factor. These average values ​​show that high illuminance resulted in significantly longer latencies than low illuminance. The study concluded that lighting conditions significantly affected attentional processing, as reflected by significant modulation of EEG activity.

[0004] Related research (Figueiro et al. - Preliminary evidence that both blue and red light can induce alertness at night.BMC Neuroscience 2009; 10: 105-105) shows that both short and long wavelength light increase nighttime alertness as shown by EEG power changes. Furthermore, 10lx red light was also found to have a significant effect on EEG measurements compared to preceding dark conditions. In another study (Plitnick et al - The effects of red and blue light on alertness and mood at night.Lighting Research and Technology 2010; 42: 449-458), both blue and red light at two levels (10lx and 40lx) were found to increase EEG beta power.

[0005] In a related study (Lin, Jing et al - Effect of long-wavelength light on electroencephalogram and subjective alertness, Lighting Research and Technology, 2020 / 01 / 05, Vol.52), the effect of exposure to two different levels of long-wavelength light (40lx and 160lx) on objective alertness (measured by EEG) was investigated. A significant effect of light level on EEG beta (13-30Hz) power was observed. Exposure to both 40lx and 160lx long-wavelength light significantly increased beta power compared to dim conditions.

[0006] In a related study (Ackeren et al. - A (blue) light in the dark: Blue light modulates oscillatory alpha activity in the occipital cortex of totally visually blind individuals with intact non-visual photoreception), three participants who were visually blind but had normal non-visual responses were subjected to on-off patterns of blue light. The study concluded that blue light affects the occipital region of the brain, decreasing the power of the alpha EEG rhythm in this particular part of the brain.

[0007] The light that affects brain signals may come from artificial lighting and / or natural daylight. The natural daylight present in a room may depend on the time of day and / or the current position of window blinds. Summary of the Invention [Problem to be solved by the invention]

[0008] The inventors have recognized that light effects, such as effects involving significant amounts of blue light, bright light or dynamics, or certain wavelengths of light, may impair EEG-based device control when utilizing the occipital region of the brain. As a result, although the BCI can look at EEGs from various regions of the brain, these EEGs may not reflect the correct cues for controlling the device because the resulting EEGs may be affected by lighting (e.g., the EEGs are attenuated or amplified). This may result in false or incorrect triggering. Therefore, it is an object of the present invention to provide a brain-controlled interface system that reduces the possibility of false / incorrect triggering. [Means for solving the problem]

[0009] According to a first aspect of the present invention, the object is to provide a method for producing a a brain-controlled interface configured to detect brain signals indicative of a user's brain activity in an environment; an input configured to obtain data indicative of a current light scene of one or more lighting devices in an environment; a lighting controller configured to control one or more lighting devices; one or more processors configured to analyze the brain signal to identify a level of noise in the brain signal when a current light scene is active, and if the level of noise exceeds a threshold, adjust the light scene while monitoring the level of noise until a target level of noise in the brain signal is established; This is achieved by a brain-controlled interface system including:

[0010] By adjusting the light scene of one or more lighting devices (and therefore the light output of one or more lighting devices), the impact of the light output on the level of noise in the brain signal is reduced. Since the light effects provided by one or more lighting devices affect the brain signal, it is beneficial to adjust the light scene to reduce the impact of the light effects. By adjusting the light scene, the brain-controlled interface system reduces the possibility of false / incorrect triggering. Adjusting the light scene may be to incrementally adjust the lighting scene.

[0011] The light scene is a dynamic light scene that changes over time, and the dynamic light scene may have a dynamics level. The one or more processors may be configured to adjust the dynamics level until a target level of noise in the brain signal is established. The level of dynamics may be defined by the amount of change in characteristics of the light output of one or more lighting devices within a period of time. The characteristics of the light output may include, but are not limited to, hue, saturation, brightness, flicker, beam direction, etc. Dynamic effects, more specifically, dynamic effects with higher dynamics levels, may affect the brain signal and result in higher levels of noise. Adjusting (reducing) the dynamics level is beneficial as it reduces the possibility of false / incorrect triggering.

[0012] The one or more processors may be configured to iteratively adjust the light scene while monitoring the level of noise until a target level of noise in the brain signal is established. The one or more processors may repeat the adjustment by further adjusting the light scene while monitoring the level of noise until the target level is established.

[0013] The one or more processors may be configured to adjust the light scene towards a target light scene until a target level of noise in the brain signal is established. The target light scene may be a predefined light scene. The adjustment may be incremental adjusting. The predefined light scene may include light output characteristics that reduce the level of noise in the detected brain signal. Adjusting the light scene (repeatedly / continuously / incrementally) to the target light scene (until a target level of noise in the brain signal is established) is beneficial because the (original) light scene is maintained as much as possible while reducing the level of noise. The target light scene may include at least one of the following characteristics: high intensity compared to the light scene, low level of dynamics compared to the light scene, and spectrum including more blue light compared to the light scene. These characteristics are examples of characteristics that affect (reduce) the level of noise.

[0014] The one or more processors may be configured to analyze the brain signal to derive control commands for the controllable device from the brain signal. The one or more processors may be further configured to switch to a brain-controlled mode when a target level of noise in the brain signal is established, and in the brain-controlled mode, the one or more processors may be configured to control the controllable device based on the derived control commands. The one or more processors may control (directly or indirectly) the controllable device based on the derived control commands only if the brain-controlled mode is active. This is beneficial since the controllable device is not controlled if the level of noise is (still) above a threshold. The one or more processors may be further configured to select the target level of noise based on the control commands expected to be provided by the user. Similarly, the one or more processors may be further configured to select the target level of noise based on an expected emotional state of the user, for example, if the user has just started meditating, the user may be expected to transition from a neutral emotional state to a relaxed emotional state.

[0015] The controllable device may be a lighting device among one or more lighting devices. Alternatively, the controllable device may be, for example, a connected (home) appliance or a connected (office) equipment.

[0016] The input may be configured to obtain data indicative of the current light scene by obtaining sensor data from a light sensor (located in the environment). Alternatively, the input may be a receiver configured to receive data indicative of the current light scene from a lighting system controller. The lighting system controller may for example be a central (home) lighting controller, a bridge, a smartphone, etc.

[0017] The current light scene may be provided by a plurality of lighting devices. The one or more processors may be configured to obtain position and / or orientation information indicative of a position and / or orientation of a user relative to the plurality of lighting devices. The one or more processors may be configured to select one or more of the plurality of lighting devices based on the position and / or orientation of the user relative to the plurality of lighting devices, and adjust the light scene by adjusting the light output of the one or more selected lighting devices. The one or more processors may be configured to select, for example, one or more lighting devices located in the user's field of view (or whose light effects are located in the user's field of view). Apart from artificial lighting, natural daylight present in the room may also affect the brain signals. One or more window blinds located in the user's field of view (or whose light effects are located in the user's field of view) may be selected and controlled accordingly. Selecting one or more lighting devices based on the user's position and / or orientation is beneficial as it optimizes the reduction of noise in the detected brain signals. Furthermore, if multiple users are present in the environment, the light scene for a particular user may be adjusted while minimizing the impact of changes in the light scene on other users.

[0018] The one or more processors may be configured to start identifying the noise level and adjusting the light scene upon receiving a control input indicating that the user will provide a control command. The control input may be a direct command or may be based, for example, on a change in the user's emotional state (which may indicate or precede the control input). This is beneficial because the one or more processors do not need to continuously monitor the noise level, but only when required, when the user intends to adjust it to provide a control command. The control input may be provided by the user, by a sensor, or by a brain-controlled interface. The control input may be received, for example, from the user, from a user interface, a sensor, a memory, etc.

[0019] According to a second aspect of the invention, the object is to provide a method for adjusting a light scene, the method comprising the steps of: detecting, by the brain-controlled interface, brain signals indicative of a user's brain activity in an environment; obtaining data indicative of a current light scene of one or more lighting devices in an environment; analyzing the EEG signal to identify a level of noise in the brain signal when the current light scene is active; if the level of noise exceeds a threshold, adjusting the light scene while monitoring the level of noise until a target level of noise in the brain signal is established; This is achieved by a method comprising:

[0020] According to a third aspect of the present invention, the above object is achieved by a computer program product for a computing device comprising computer program code for performing the above mentioned method when the computer program product is executed on a processing unit of the computing device.

[0021] It should be understood that the method and computer program product may have similar and / or identical embodiments and advantages as the brain-controlled interface system described above.

[0022] In the context of the present invention, the term "light scene" relates to lighting control instructions / light settings for one or more lighting devices. The lighting control instructions may be the same for each lighting device or may be different for different lighting devices. The lighting control instructions may relate to one or more light settings, which may be defined, for example, as RGB / HSL / HSB color values, CIE color values, luminance values, etc. [Brief description of the drawings]

[0023] The above and additional objects, features and advantages of the disclosed system, device and method will be better understood through the following illustrative and non-limiting detailed description of embodiments of the device and method, with reference to the accompanying drawings, in which: All figures are schematic and not necessarily to scale, and generally show only those parts necessary to clarify the invention, other parts may be omitted or merely suggested. [Figure 1] FIG. 1 shows a schematic diagram of an example of a brain-controlled interface system. [Figure 2a] FIG. 2a shows a schematic diagram of an example of a set of detected brain signals under the influence of illumination. [Figure 2b] FIG. 2b shows a schematic example of the set of brain signals of FIG. 2a under different illumination. [Figure 3a] FIG. 3a shows a schematic diagram of an example of a set of detected brain signals under the influence of illumination. [Figure 3b] FIG. 3b shows a schematic example of the set of brain signals of FIG. 3a under different illumination. [Figure 4] FIG. 4 illustrates generally one example of a system in which a lighting device is selected based on a user's position and / or orientation relative to a number of lighting devices. [Diagram 5] FIG. 5 illustrates a schematic diagram of one example of a method for adjusting a light scene. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] FIG. 1 shows a schematic overview of a brain-controlled interface system 100. The brain-controlled interface system 100 includes a brain-controlled interface 120 (e.g., a head-mounted device). The brain-controlled interface 120 (BCI) is configured to detect brain signals indicative of brain activity of a user 160 in an environment 150. The system 100 further includes one or more processors 106 configured to analyze the brain signals. The BCI 120 may include one or more electrodes 122 that contact the user's scalp, and the electrodes 122 are used to detect the user's EEG signals. It should be understood that such a BCI 120 is an example and other types of brain signal detection may be used.

[0025] The brain-controlled interface system 100 further includes an input 102 configured to obtain data indicative of a current light scene of one or more lighting devices 112, 114 in the environment 150. The input 102 may be an input to one or more processors 106 configured to obtain data indicative of a current light scene of one or more lighting devices 112, 114 from a memory, e.g., memory 108. Alternatively, the input 102 may be configured to obtain data indicative of a current light scene by obtaining sensor data from a light sensor located in the environment 150. Alternatively, the input 102 may be a receiver configured to receive (wirelessly) data indicative of a current light scene, e.g., from a lighting controller 110, e.g., a central home / office control system, from a remote lighting controller connected to one or more lighting devices 112, 114 via the cloud, etc. The lighting controller 110 may be configured to control one or more lighting devices 112, 114 by communicating lighting control signals to the one or more lighting devices 112, 114 (e.g., via Zigbee, BLE, Ethernet, etc.) to generate a light scene. The control signals include light settings indicating light output characteristics (e.g., hue, saturation, brightness, beam direction, etc.). The one or more lighting devices 112, 114 are configured to receive the control signals and drivers configured to adjust the light output of one or more (LED) light sources accordingly.

[0026] The brain-control interface system 100 further includes one or more processors 106 (e.g., circuits, one or more microcontrollers, etc.). The one or more processors 106 are configured to obtain data indicative of the brain signals detected by the BCI 120. The one or more processors 106 may be included in a single device or distributed across multiple devices, which may depend on the system architecture of the BCI system 100. For example, in the example of FIG. 1, the one or more processors 106 and the input unit 102 are included in a single device 170, which is communicatively coupled to the lighting controller 110, the BCI 120, and the controllable device 130. It should be noted that this system architecture is merely an example, and that a person skilled in the art may design alternative system architectures without departing from the scope of the appended claims. For example, a first processor of the one or more processors 106 may be included in the BCI 120, and a second processor may be included in a remote server or the lighting controller 110. In another example, the one or more processors 106 and the input unit 102 may be included in the lighting controller 110. In another example, a first processor of the one or more processors 106 may be included in a remote server and a second processor may be included in the lighting controller 110. In yet another example, one or more of the system components 102, 106 may be included in the BCI 120 or the controllable device 130.

[0027] The one or more processors 106 are configured to analyze the brain signals to identify a level of noise in the brain signals when the current light scene is active. The light that affects the brain signals may come from artificial lighting and / or natural daylight. The natural daylight present in the room may depend on the time of day and / or the current position of the window blinds. The one or more processors 106 may, for example, compare the detected brain signals to a reference brain signal to determine a level of noise based on a difference between the detected brain signal and the reference brain signal. Additionally or alternatively, the one or more processors 106 may compare the detected brain signal to one or more thresholds and / or baselines to determine a level of noise. In another example, the one or more processors 106 may be configured to obtain data indicative of a current light scene of one or more lighting devices in the environment and determine which brain signals may be affected by the current light scene. For example, dynamic effects, more specifically, dynamic effects having a higher dynamics level, may affect brain signals originating from certain regions of the brain, and the one or more processors 106 may be configured to analyze these regions for noise to determine a level of noise. Light of a particular color may also affect the brain signals. For example, short wavelength light, such as blue light, affects the back of the brain, reducing the power of the alpha EEG rhythm in this particular part of the brain. Similarly, long wavelength light, such as red light, affects the beta EEG rhythm. The one or more processors 106 may be configured to analyze particular regions associated with the light scene for noise to determine the level of noise.

[0028] The one or more processors 106 may further be configured to determine whether the level of noise exceeds a threshold, and if so, adjust the light scene (by controlling one or more lighting devices 112, 114 via the lighting controller 110) while monitoring the level of noise until a target level of noise in the brain signal is established. The one or more processors 106 may, for example, change the color, brightness, or level of dynamics of the light. Certain light effects have less impact on the brain signal than others, and the one or more processors 106 may be configured to adjust the light scene such that the adjusted light scene has a reduced impact on the brain signal. The one or more processors 106 may, for example, increase the intensity of the light scene, adjust the color point of the light scene (e.g., towards a more bluish light), reduce the level of dynamics of the dynamic light scene, etc. The one or more processors 106 may, for example, control the one or more lighting devices 112, 114 to gradually adjust the light scene until a target level of noise in the brain signal is established. The one or more processors 106 may, for example, gradually change the light scene from a current light scene to a target light scene. Alternatively, the one or more processors may be configured to iteratively adjust the light scene while monitoring the level of noise until a target level of noise in the brain signal is established. In other words, the one or more processors 106 may control the one or more lighting devices 112, 114 sequentially according to different light scenes until a light scene is activated at which the level of noise in the brain signal is below a threshold.

[0029] 2A shows a graph that diagrammatically illustrates a brain signal s captured over time t versus a threshold th. The one or more processors 106 may determine that the intensity i of the brain signal s exceeds a threshold th, which may indicate that the level of noise exceeds the threshold. Based on this, the one or more processors 106 may adjust the active light scene to reduce the level of noise, resulting in a brain signal s that does not exceed the threshold th, as shown in FIG. 2b. Alternatively, although not shown, the thresholds may include both an upper threshold and a lower threshold, and if the brain signal does not exceed the upper and lower thresholds, the brain signal no longer exceeds the threshold.

[0030] FIG. 3a shows another example of a graph in which a set of brain signals are detected. The letters A-E indicate different brain regions, and the length of the bars indicates the level of (changes in) brain activity in the different brain regions. Each brain signal A-E may correspond to an electrode positioned on the user's scalp. The one or more processors 106 may determine that the intensity of brain signal C exceeds a threshold th1, which may indicate that the level of noise for brain region C exceeds the threshold. Based on this, the one or more processors 106 may adjust the active light scene to reduce the level of noise, resulting in brain signals A-E of FIG. 3b that do not exceed threshold th1.

[0031] The one or more processors 106 may be configured to adjust the light scene towards a target light scene until a target level of noise in the brain signal is established. The target light scene may be a predefined light scene. The predefined light scene may include light output characteristics that reduce the level of noise in the detected brain signal. By gradually adjusting the light scene towards the target light scene, the original (current) light scene is maintained as much as possible while reducing the level of noise. The target light scene may include at least one of the following characteristics: high intensity compared to the light scene, low level of dynamics compared to the light scene, and spectrum including more blue light compared to the light scene. These characteristics are examples of characteristics that affect (reduce) the level of noise. For example, if the current light scene is a reddish light scene, the one or more processors 106 may adjust the light scene towards a bluish target light scene until a target level of noise in the brain signal is established. If the target level of noise is established during the adjustment (transition) towards the bluish light scene, the one or more processors 106 may stop adjusting towards the target bluish light scene. In another example, the current light scene may be a low brightness (e.g., 10%) light scene, and the one or more processors 106 may adjust the light scene towards a higher brightness (e.g., 100%) light scene until a target level of noise in the brain signal is established. If a target level of noise is established at a certain brightness (e.g., 30%), the one or more processors 106 may stop adjusting towards a target light scene of higher brightness (e.g., 100%). In another example, the current light scene may be a dynamic light scene with a (high) level of dynamics, and the one or more processors 106 may adjust the light scene towards a target dynamic light scene with a lower level of dynamics until a target level of noise in the brain signal is established. If a target level of noise is established at a certain level of dynamics, the one or more processors 106 may stop adjusting towards a target light scene with a lower level of dynamics.

[0032] The one or more processors 106 may be configured to analyze the brain signals to derive control commands for the controllable device 130 from the brain signals. The controllable device may be a lighting device of the one or more lighting devices. Alternatively, the controllable device 130 may be, for example, a connected (home) appliance or a connected (office) appliance. The one or more processors 106 may be further configured to switch to a brain-controlled mode when a target level of noise in the brain signals is established, and in the brain-controlled mode, the one or more processors 106 may be configured to control the controllable device 130 based on the derived control commands. The one or more processors 106 may control (directly or indirectly) the controllable device 130 based on the derived control commands only when the brain-controlled mode is active.

[0033] The one or more processors 106 may further be configured to select a target level of noise based on a control command expected to be provided by a user. A lower level of noise may be required for certain control commands, which may require distinguishing between different control commands, which may be control commands for the same device / service. Thus, the one or more processors 106 may be configured to obtain an expected control command and to select a target level of noise based on the expected control command. The expected control command may be obtained from a memory or may be obtained, for example, from a machine learning system that has learned over time which control commands have been provided.

[0034] Additionally or alternatively, the one or more processors 106 may be configured to analyze the brain signals to derive information about the user, for example, information about the user's mental / emotional state. The current light scene may affect the brain signals such that the level of noise is too high for the one or more processors 106 to derive this information. If the one or more processors 106 determine that the level of noise exceeds a threshold, the one or more processors may adjust the light scene to reduce the noise and control the one or more lighting devices 112, 114 to properly derive information about the user.

[0035] The current light scene may be provided by the multiple lighting devices 112, 114. The one or more processors 106 may be configured to obtain position and / or orientation information indicative of a position and / or orientation of a user 160 relative to the multiple lighting devices 112, 114. The one or more processors 106 may be configured to select one or more of the multiple lighting devices based on the position and / or orientation of the user 160 relative to the multiple lighting devices 112, 114, and adjust the light scene by adjusting the light output (only) of the one or more selected lighting devices 112, 114. The positions of the multiple lighting devices 110, 112, 114, 116 relative to the user may be obtained (e.g. via an input) from an (indoor) positioning system, e.g. an RF-based positioning system, a coded light positioning system, a camera-based positioning system, from an internal memory, etc. Alternatively, the positions of the lighting devices 110, 112, 114, 116 may be defined by a user via a user interface, and the user may provide information regarding the positions of the lighting devices 112, 114, for example, by locating virtual counterparts of the lighting devices on a map of the environment in which the lighting devices are located. The user may further indicate a typical user position (and orientation) on the map. Such techniques for determining the location of the lighting devices in the environment relative to the user are known in the art and therefore will not be discussed in detail. The one or more processors 106 may be configured to select one or more lighting devices that are located in the user's field of view (or whose light effects are located in the user's field of view) based on the user's position and / or orientation relative to the lighting devices 112, 114. If multiple users are present in the environment, the light scene for a particular user may be adjusted while minimizing the impact of changes in the light scene on other users.

[0036] 4 illustrates generally one example of a system in which lighting devices 210, 212, 214, 216 are selected based on a position and / or orientation of a user 160 relative to the multiple lighting devices 210, 212, 214, 216. The one or more processors 106 may, for example, determine that lighting devices 210 and 212 are located closer to (and within the user's field of view of) a user 240 compared to lighting devices 214 and 216. Based on this, the one or more processors 106 may select lighting devices 210 and 212 and adjust the light scene by adjusting the light output of the one or more selected lighting devices 210 and 212. The light output of non-selected lighting devices 214 and 216 may be maintained or adjusted to a lesser extent compared to adjusting the light output of the selected lighting devices 210 and 212.

[0037] The one or more processors 106 may be configured to start identifying the noise level and adjusting the light scene upon receiving a control input indicating that the user will provide a control command. Thus, the one or more processors 106 may monitor the noise level only when required when the user intends to adjust to provide a control command. The control input may be provided by the user, by a sensor, or by a brain-controlled interface. The control input may be received, for example, from the user or from a user interface (e.g., a user interface of an augmented reality device, a voice assistant, a smart device, etc.). Alternatively, a (remote or local) sensor may detect that the user is about to provide a control command. Alternatively, the BCI may determine that the user is in the process of generating or will generate a control command based on the detected brain signals. Alternatively, the one or more processors may access a memory that stores times when the user typically provides a control command, and start identifying the noise level and adjusting the light scene at these times.

[0038] 5 illustrates generally an example of a method for adjusting a light scene. The method 500 includes detecting 502, by a brain control interface, brain signals indicative of a user's brain activity in the environment, obtaining 504 data indicative of a current light scene of one or more lighting devices in the environment, analyzing 506 the EEG signals to identify a level of noise in the brain signals when the current light scene is active, and if the level of noise exceeds a threshold, adjusting 508 the light scene while monitoring the level of noise until a target level of noise in the brain signals is established.

[0039] The method 500 may be performed by computer program code of a computer program product when the computer program product is executed on a processing unit of a computing device, such as one or more of the one or more processors 106 of the system 100.

[0040] It should be noted that the above-described embodiments are illustrative rather than limiting of the invention, and that those skilled in the art can design many alternative embodiments without departing from the scope of the appended claims.

[0041] In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb "comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer or processing unit. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage.

[0042] Aspects of the invention may be embodied in a computer program product, which may be a collection of computer program instructions stored on a computer-readable storage device that may be executed by a computer. The instructions of the invention may be any interpretable or executable code mechanism, including, but not limited to, a script, an interpretable program, a dynamic link library (DLL), or a Java class. The instructions may be provided as a complete executable program, a partial executable program, a modification (e.g., an update) to an existing program, or an extension (e.g., a plug-in) to an existing program. Additionally, parts of the processing of the invention may be distributed across multiple computers or processors, or a "cloud."

[0043] Suitable storage media for storing computer program instructions include all forms of non-volatile memory, including, but not limited to, EPROM, EEPROM and flash memory devices, magnetic disks such as internal and external hard disk drives, removable disks and CD-ROM disks. The computer program may be distributed on such storage media or may be made available for downloading via HTTP, FTP, email or a server connected to a network such as the Internet.

Claims

1. a brain-controlled interface configured to detect brain signals indicative of a user's brain activity in an environment; an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment; a lighting controller configured to control the one or more lighting devices; one or more processors configured to analyze the brain signals to identify a level of noise in the brain signals when the current light scene is active, and if the level of noise exceeds a threshold, adjust the light scene while monitoring the level of noise until a target level of noise in the brain signals is established; A brain-controlled interface system comprising:

2. 2. The brain-controlled interface system of claim 1, wherein the light scene is a dynamic light scene that changes over time, the dynamic light scene having a dynamics level, and the one or more processors are configured to adjust the dynamics level until a target level of noise in the brain signal is established.

3. 3. The brain-controlled interface system of claim 1 or 2, wherein the one or more processors are configured to iteratively adjust the light scene while monitoring the level of noise until a target level of noise in the brain signal is established.

4. 4. The brain-controlled interface system of claim 1, wherein the one or more processors are configured to adjust the light scene towards a target light scene until a target level of noise in the brain signal is established.

5. The target light scene is a high intensity compared to said light scene, a lower level of dynamics compared to the light scene; A spectrum that includes more blue light than the light scene; and a spectrum that contains more red light than the light scene; The brain-controlled interface system of claim 4, further comprising at least one of the following characteristics:

6. 6. The brain-controlled interface system of claim 1 , wherein the one or more processors are configured to analyze the brain signals to derive control commands for a controllable device from the brain signals, and the one or more processors are configured to switch to a brain control mode when a target level of noise in the brain signals is established, and in the brain control mode, the one or more processors are configured to control the controllable device based on the derived control commands.

7. The brain-controlled interface system of claim 6 , wherein the controllable device is a lighting device of the one or more lighting devices.

8. 8. A brain-controlled interface system according to claim 6 or 7, wherein the one or more processors are configured to select the target level of noise based on control commands expected to be provided by a user.

9. 9. The brain-controlled interface system of claim 1, wherein the input unit is configured to obtain data indicative of the current light scene by obtaining sensor data from a light sensor.

10. 10. The brain-controlled interface system of claim 1, wherein the input is a receiver arranged to receive data indicative of the current light scene from a lighting system controller.

11. the current light scene is provided by a plurality of lighting devices; the one or more processors are configured to obtain position and / or orientation information indicative of a position and / or orientation of a user relative to the plurality of lighting devices; 11. The brain-controlled interface system of claim 1, wherein the one or more processors are configured to select one or more of the plurality of lighting devices based on a user's position and / or orientation relative to the plurality of lighting devices, and to adjust a light scene by adjusting the light output of the one or more selected lighting devices.

12. 12. The brain-controlled interface system of claim 1, wherein the one or more processors are configured to initiate identifying noise levels and adjusting the light scene upon receiving a control input indicating that a user will be providing a control command.

13. The brain-controlled interface system of claim 12 , wherein the control input is provided by a user, by a sensor or by the brain-controlled interface.

14. 1. A method for adjusting a light scene, the method comprising: detecting, by the brain-controlled interface, brain signals indicative of a user's brain activity in an environment; obtaining data indicative of a current light scene of one or more lighting devices in the environment; analyzing the EEG signal to identify a level of noise in the brain signal when the current light scene is active; if the level of noise exceeds a threshold, adjusting the light scene while monitoring the level of noise until a target level of noise in the brain signal is established; A method comprising:

15. 15. A computer program product for a computing device comprising computer program code for performing the method according to claim 14 when said computer program product is executed on a processing unit of said computing device.

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