CN118592092A - Lighting control for brain control interface system - Google Patents

Abstract

A brain control interface system is disclosed. The brain control interface includes: a brain control interface configured to detect brain signals indicative of brain activity of a user 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; and one or more processors configured to analyze the brain signal to identify a noise level in the brain signal when the current light scene is active, and adjust the light scene while monitoring the noise level if the noise level exceeds a threshold until a target level of noise in the brain signal has been established.

Description

Lighting control for brain control interface system

Technical Field

The present invention relates to a brain control interface system. The invention also relates to a method of adjusting a light scene and a computer program for performing the method.

Background

Brain wave (Brainwave) based device control is an emerging technology. A brain-computer interface (BCI) is used to detect brain signals of a user, from which information is derived. The information may, for example, indicate the user's mind or action. The idea may for example indicate control commands for controllable devices such as lighting devices. An example of such a system is disclosed in US10551921B 2. There are two main types of BCIs: non-invasive and invasive BCI. Non-invasive versions are the most common and include sensors (electrodes) placed on the head of a person. These measure brain activity and translate the data to a computer. Most BCIs utilize an electroencephalogram (EEG) system, which is typically characterized by electrodes attached to the scalp that measure the current sent by neurons in the brain. This change in current reflects brain activity because hundreds of thousands of neurons are excited when an individual performs an action or thinks about something. This generates a current large enough to be measured on the scalp. The computer system then attempts to understand this data to derive the user's actions or ideas. Alternatives to EEG systems are Electrooculogram (EOG), electromyogram (EMG), electrodermal activity (EDA) and photoplethysmography (PPG) systems. As an alternative to utilizing electrodes on the scalp surface, an implantable brain-computer interface may be used. Here, the probe is inserted into the brain through an automated process performed by the surgical robot. Each probe comprises: a lead region (area of wire) containing electrodes capable of locating electrical signals in the brain, and a sensory region (sensory area) in which the lead interacts with an electronic system that allows for amplification and acquisition of brain signals.

An investigation study (Min et al -Bright Illumination Reducesparietal EEG alpha activity during a sustained attention task,Brain research,2013) conducted several experiments with sustained attention to subjects under different light conditions EEG was recorded from the top lobe region of the brain. This study found that brain pulses were significantly affected by factors of illuminance. Their mean values indicated that high illuminance resulted in significantly longer latency than low illuminance. The study concluded that light conditions significantly affected attention treatment as reflected in significant modulation of EEG activity.

One related study (fig. et al -Preliminary evidence that both blue and red light can induce alertness at night,BMC Neuroscience 2009;10:105-105) showed that both short and long wavelength light improved night alertness as shown in EEG power changes, in addition, 10lx red light was also found to significantly affect EEG measurements compared to previous dark conditions in another study (Plitnick et al -The effects ofred 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 (10 lx and 40 lx) were found to increase EEG beta (beta) power.

In a related study (Lin, jin et al -Effect oflong-wavelength light on electroencephalogram and subje1ctive alertness,Lighting Research and Technology,2020/01/05,Vol.52), et al), it was investigated how exposure to two different levels (40 lx and 160 lx) of long wavelength light affected objective alertness (as measured by EEG).

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-visualphotoreception)), three participants, who were visually blind but had complete non-visual responses, experienced a switching pattern of blue light.

Light affecting brain signals may originate from artificial lighting and/or natural sunlight. The natural daylight present in the room may depend on the time of day and/or the current position of the blinds.

Disclosure of Invention

The inventors have realized that light effects (e.g., effects including substantial blue, bright or dynamic light, or light of a specific wavelength) can compromise brain wave based device control when utilizing occipital regions. Thus, the BCI may view brain waves of different areas of the brain, but these brain waves may not reflect the correct cues for device control, as the resulting brain waves may be affected by the illumination (e.g., the brain waves are attenuated or amplified). This may lead to false or incorrect triggers. It is therefore an object of the present invention to provide a brain control interface system that reduces the chance of false/incorrect triggers.

According to a first aspect of the invention, this object is achieved by a brain control interface system comprising:

A brain control interface configured to detect brain signals indicative of brain activity of a user 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 one or more lighting devices, and

-One or more processors configured to analyze the brain signal to identify a noise level in the brain signal when the current light scene is active, and to adjust the light scene while monitoring the noise level if the noise level exceeds a threshold until a target level of noise in the brain signal has been established.

By adjusting the light scene of the one or more lighting devices (and adjusting the light output of the one or more lighting devices therewith), the effect of the light output on the noise level in the brain signal is reduced. Since the light effect provided by the one or more lighting devices affects the brain signal, it is beneficial to adjust the light scene to reduce the effect of the light effect. By adjusting the light scene, the brain control interface system reduces the chance of false/incorrect triggers. The adjustment of the light scene may be a progressive adjustment of the illumination scene.

The light scene may be a dynamic light scene that varies over time, wherein the dynamic light scene has a dynamic level. The one or more processors may be configured to adjust the dynamic level until a target level of noise in the brain signal has been established. The dynamic level may be defined by the amount of change in the property of the light output of the one or more lighting devices over a period of time. Attributes of the light output may include, but are not limited to: hue, saturation, brightness, flicker, beam direction, etc. Dynamic effects, and more particularly dynamic effects with higher dynamic levels, can affect brain signals and result in higher levels of noise. Adjusting (reducing) the dynamic level is beneficial because it reduces the chance of false/incorrect triggers.

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

The one or more processors may be configured to adjust the light scene toward the target light scene until a target level of noise in the brain signal has been established. The target light scene may be a predefined light scene. The adjustment may be a progressive adjustment. The predefined light scene may include light output characteristics that reduce noise levels in the detected brain signal.

It is beneficial to (iteratively/continuously/progressively) adjust the light scene to the target light scene until the target level of noise in the brain signal has been established, since the (original) light scene is kept as much as possible while the noise level is reduced. The target light scene may include at least one of the following characteristics: higher intensity compared to light scenes, lower dynamic level compared to light scenes, and a spectrum comprising more blue light compared to light scenes. These characteristics are examples of characteristics that affect (reduce) the noise level.

The one or more processors may be configured to analyze the brain signals to derive control commands for the controllable device from the brain signals. The one or more processors may be further configured to switch to a brain control mode when a target level of noise in the brain signal has been established, wherein in the brain control mode the one or more processors are configured to control the controllable device based on the derived control commands. The one or more processors may control the controllable device only (directly or indirectly) based on the derived control commands only when the brain control mode is active. This is beneficial because the controllable device is not controlled when the noise level (still) exceeds the threshold. The one or more processors may be further configured to select a target level of noise based on the desired control command 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, e.g. when the user just starts meditation, e.g. the user may be expected to transition from a neutral emotional state to a relaxed emotional state.

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

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 the lighting system controller. The lighting system controller may be, for example, a central (home) lighting controller, a bridge, a smart phone, etc.

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: one or more of the plurality of lighting devices is selected based on a position and/or orientation of the user relative to the plurality of lighting devices, and the light scene is adjusted by adjusting a light output of the one or more selected lighting devices. The one or more processors may, for example, be configured to select one or more lighting devices that are located in (or whose light effects are located in) the field of view of the user. In addition to artificial lighting, the presence of natural daylight in a room may also affect brain signals. One or more blinds located in (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 position and/or orientation of the user is beneficial because it optimizes the reduction of noise in the detected brain signal. In addition, when multiple users will be 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.

The one or more processors may be configured to initiate identifying a noise level and adjusting the light scene upon receiving a control input indicating that the user is to provide a control command. The control input may be a direct command or based on, for example, a change in the emotional state of the user (which may indicate or precede the control input). This is advantageous because the one or more processors need not monitor the noise level continuously, but only when the user intends to adjust to provide control commands. The control input may be provided by the user, by a sensor or by a brain control interface. The control input may be received, for example, from a user interface, sensors, memory, etc.

According to a second aspect of the invention, the object is achieved by a method of adjusting a light scene, the method comprising:

Detecting brain signals indicative of brain activity of a user in an environment through a brain control interface,

Obtaining data indicative of a current light scene of one or more lighting devices in the environment,

Analyzing the EEG signal to identify a noise level in the brain signal when the current light scene is active,

-Adjusting the light scene while monitoring the noise level in case the noise level exceeds a threshold value, until a target level of noise in the brain signal has been established.

According to a third aspect of the invention, the object is achieved by a computer program product for a computing device, comprising computer program code for performing the method when the computer program product is run on a processing unit of the computing device.

It should be appreciated that the method and computer program product may have similar and/or identical embodiments and advantages as the brain control interface system described above.

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.

Drawings

The foregoing and additional objects, features and advantages of the disclosed system, apparatus and method will be better understood from the following illustrative and non-limiting detailed description of embodiments of the apparatus and method with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an example of a brain control interface system;

FIG. 2a schematically shows an example of a set of detected brain signals affected by illumination;

FIG. 2b schematically shows an example of a set of brain signals of FIG. 2a under different illumination;

FIG. 3a schematically shows an example of a set of detected brain signals affected by illumination;

FIG. 3b schematically shows an example of a set of brain signals of FIG. 2a under different illumination;

FIG. 4 schematically illustrates an example of a system in which a lighting device is selected based on a user's position and/or orientation with respect to a plurality of lighting devices; and

Fig. 5 schematically illustrates an example of a method of adjusting a light scene.

All the figures are schematic, not necessarily to scale, and generally show only parts which are necessary in order to elucidate the invention, wherein other parts may be omitted or merely suggested.

Detailed Description

Fig. 1 schematically shows an overview of a brain control interface system 100. The brain control interface system 100 includes a brain control interface 120 (e.g., a head-mounted device). The brain control interface 120 (BCI) is configured to detect brain signals indicative of brain activity of the user 160 in the 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 in contact with the scalp of the user, the electrodes 122 for detecting EEG signals of the user. It should be appreciated that such a BCI 120 is one example, and that other types of brain signal detection may be used.

The brain control interface system 100 further comprises 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, the one or more processors 106 being configured to obtain data from a memory (e.g., the memory 108) indicative of a current light scene of the one or more lighting devices 112, 114. Alternatively, the input 102 may be configured to obtain data indicative of the 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 (wirelessly) receive data indicative of the current light scene, e.g. from a lighting controller 110 such as a central home/office control system, from a remote lighting controller connected to one or more lighting devices 112, 114 via a cloud, etc. The lighting controller 110 may be configured to control the one or more lighting devices 112, 114 to generate the light scene by transmitting lighting control signals to the one or more lighting devices 112, 114 (e.g., via Zigbee, BLE, ethernet, etc.). The control signal includes a light setting indicating a light output attribute (e.g., hue, saturation, brightness, beam direction, etc.). The one or more lighting devices 112, 114 are configured to receive the control signal, and the driver is configured to adjust the light output of the one or more (LED) light sources accordingly.

The brain control interface system 100 further includes one or more processors 106 (e.g., circuitry, one or more microcontrollers, etc.). The one or more processors 106 are configured to obtain data indicative of brain signals as 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 102 are included in a single device 170, the device 170 being communicatively coupled with the lighting controller 110, the BCI 120, and the controllable device 130. It should be understood that this system architecture is merely an example, and that a skilled person is able to 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 on a remote server or in the lighting controller 110. In another example, one or more processors 106 and inputs 102 may be included in 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 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 in the controllable device 130.

The one or more processors 106 are configured to analyze the brain signal to identify a noise level in the brain signal when the current light scene is active. Light affecting brain signals may originate from artificial lighting and/or natural sunlight. The natural daylight present in the room may depend on the time of day and/or the current position of the blinds. The one or more processors 106 may, for example, compare the detected brain signal to a reference brain signal to determine a noise level 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 signals to one or more thresholds and/or baselines to determine a noise level. In another example, the one or more processors 106 may be configured to: data indicative of a current light scene of one or more lighting devices in the environment is obtained and it is determined which brain signals are likely to be affected by the current light scene. For example, dynamic effects, and more particularly dynamic effects with higher dynamic levels, may affect brain signals originating from certain regions of the brain, and the one or more processors 106 may be configured to analyze noise of those regions to determine noise levels. Certain colors of light may also affect brain signals. For example, short wavelength light such as blue light affects the occipital region of the brain and reduces the power of the alpha EEG rhythms in that particular portion of the brain. Similarly, long wavelength light such as red affects beta EEG rhythms. The one or more processors 106 may be configured to analyze noise of a particular region associated with the light scene to determine a noise level.

The one or more processors 106 are further configured to determine whether the noise level exceeds a threshold, and if so, adjust the light scene while monitoring the noise level (by controlling the one or more lighting devices 112, 114 via the lighting controller 110) until a target level of noise in the brain signal has been established. The one or more processors 106 may, for example, change the color, brightness, or dynamic level of the light. Some light effects have less impact on brain signals than others, and the one or more processors 106 may be configured to adjust the light scene such that the adjusted light scene reduces the impact on brain signals. 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., toward bluer light), decrease the dynamic level of the dynamic light scene, and so forth. The one or more processors 106 may, for example, control the one or more lighting devices 112, 114 and gradually adjust the light scene until a target level of noise in the brain signal has been established. The one or more processors 106 may, for example, gradually change the light scene from the current light scene to the target light scene. Alternatively, the one or more processors may be configured to iteratively adjust the light scene while monitoring the noise level until a target level of noise in the brain signal has been established. In other words, the one or more processors 106 may sequentially control the one or more lighting devices 112, 114 according to different light scenes until a light scene in the brain signal with a noise level below a threshold is active.

Fig. 2a shows a graph schematically illustrating a brain signal s and a threshold th captured over time t. 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 noise level exceeds a threshold. Based thereon, the one or more processors 106 may adjust the active light scene to reduce the noise level, thereby generating a brain signal s that does not exceed the threshold th as illustrated in fig. 2 b. Alternatively, not depicted, the threshold may include both an upper threshold and a lower threshold, wherein if the brain signal no longer exceeds the upper threshold and the lower threshold, the brain signal no longer exceeds the threshold.

Fig. 3a shows another example of a graph in which a set of brain signals is detected. Letters a-E indicate different brain regions, and the length of the bars indicates the level of brain activity (variation) for the different brain regions. Each brain signal a-E may correspond to an electrode placed on the scalp of the user. The one or more processors 106 may determine that the intensity of the brain signal C exceeds a threshold th1, which may indicate that the noise level of the brain region C exceeds the threshold. Based thereon, the one or more processors 106 may adjust the active light scene to reduce the noise level, thereby generating brain signals a-E of fig. 3b that do not exceed the threshold th1.

The one or more processors 106 may be configured to adjust the light scene toward the target light scene until a target level of noise in the brain signal has been established. The target light scene may be a predefined light scene. The predefined light scene may include light output characteristics that reduce noise levels in the detected brain signal. By gradually adjusting the light scene towards the target light scene, the noise level is reduced while keeping as much of the original (current) light scene as possible. The target light scene may include at least one of the following characteristics: higher intensity compared to light scenes, lower dynamic level compared to light scenes, and a spectrum comprising more blue light compared to light scenes. These characteristics are examples of characteristics that affect (reduce) the noise level. If, for example, the current light scene is a reddish light scene, the one or more processors 106 may adjust the light field Jing Chaoxiang with a blue target light scene until a target level of noise in the brain signal has been established. The one or more processors 106 may cease adjustment toward the target bluish light scene if the target level of noise has been established during the adjustment (transition) toward the bluish light scene. In another example, the current light scene may be a light scene with low brightness (e.g., 10%) and the one or more processors 106 may adjust the light scene toward a light scene with higher brightness (e.g., 100%) until a target level of noise in the brain signal has been established. If at a certain brightness (e.g., 30%) the target level of noise has been established, the one or more processors 106 may stop the adjustment towards the target light scene with a higher brightness (e.g., 100%). In another example, the current light scene may be a dynamic light scene having a (high) dynamic level, and the one or more processors 106 may adjust the light scene toward a target dynamic light scene having a lower dynamic level until the target level of noise in the brain signal has been established. If the target level of noise has been established at a certain dynamic level, the one or more processors 106 may cease adjustment toward the target light scene having a lower dynamic level.

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) equipment. The one or more processors 106 may be further configured to switch to a brain control mode when a target level of noise in the brain signal has been established, wherein in the brain control mode the one or more processors 106 are configured to control the controllable device 130 based on the derived control commands. The one or more processors 106 may control the controllable device 130 only (directly or indirectly) based on the derived control commands only when the brain control mode is active.

The one or more processors 106 may be further configured to select a target level of noise based on the desired control commands to be provided by the user. Lower noise levels may be required for some control commands, which may be necessary to distinguish between different control commands, which may be control commands for the same device/service. Accordingly, the one or more processors 106 may be configured to obtain the desired control commands and select a target level of noise based on the desired control commands. The desired control commands may be obtained from memory or, for example, from a machine learning system that has learned which control commands have been provided over time.

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

The current light scene may be provided by a plurality of 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 the user 160 relative to the plurality of lighting devices 112, 114. The one or more processors 106 may be configured to: one or more of the plurality of lighting devices is selected based on the position and/or orientation of the user 160 relative to the plurality of lighting devices 112, 114, and the light scene is adjusted by adjusting (only) the light output of the one or more selected lighting devices 112, 114. The positions of the plurality of lighting devices 110, 112, 114, 116 relative to the user may be obtained (e.g., via inputs) from a (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 locations of the plurality of lighting devices 110, 112, 114, 116 may be defined by a user via a user interface, wherein the user may provide information about the locations of the plurality of lighting devices 112, 114, for example by positioning 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 location (and orientation) on the map. Such techniques for determining the location of a lighting device in an environment relative to a user are known in the art and will therefore 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 (or whose light effects are located in) the user's field of view based on the user's position and/or orientation with respect to the plurality of lighting devices 112, 114. When there will be multiple users 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.

Fig. 4 schematically illustrates an example of a system in which the lighting devices 210, 212, 214, and 216 are selected based on the position and/or orientation of the user 160 relative to the plurality of lighting devices 210, 212, 214, 216. The one or more processors 106 may, for example, determine that the lighting devices 210 and 212 are positioned closer to the user 240 (and within the field of view of the user) than the lighting devices 214 and 216. Based thereon, the one or more processors 106 may select the 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 the unselected illumination devices 214 and 216 may be maintained or adjusted to a lesser extent than the adjustment of the light output of the selected illumination devices 210 and 212.

The one or more processors 106 may be configured to initiate identifying noise levels and adjusting light scenes upon receiving control input indicating that a user is to provide control commands. Thus, the one or more processors 106 may monitor the noise level only when needed when the user intends to adjust to provide control commands. The control input may be provided by the user, by a sensor or by a brain control interface. The control input may be received, for example, from a user interface (e.g., a user interface of an augmented reality device, a voice assistant, a smart device, etc.). Alternatively, the (remote or local) sensor may detect that the user is about to provide control commands. Alternatively, the BCI may determine that the user is in the process of generating control commands or is planning to generate control commands based on the detected brain signals. Alternatively, the one or more processors may access a memory storing the moments at which the user typically provides control commands, and initiate identifying noise levels and adjusting light scenes at these moments.

Fig. 5 schematically illustrates an example of a method of adjusting a light scene. The method 500 includes: a brain signal indicative of brain activity of a user in the environment is detected 502 by a brain control interface, data indicative of a current light scene of one or more lighting devices in the environment is obtained 504, the EEG signal is analyzed 506 to identify a noise level in the brain signal when the current light scene is active, and the light scene is adjusted 508 while monitoring the noise level if the noise level exceeds a threshold until a target level of noise in the brain signal has been established.

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

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims.

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 measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Aspects of the invention may be implemented in a computer program product, which may be a set of computer program instructions stored on a computer readable storage device that may be executed by a computer. The instructions of the present invention may be in any interpretable or executable code mechanism, including but not limited to scripts, interpretable programs, dynamic Link Libraries (DLLs), or Java classes. The instructions may be provided as a complete executable program, as a partial executable program, as a modification (e.g., update) to an existing program, or as an extension (e.g., plug-in) to an existing program. Furthermore, portions of the processing of the present invention may be distributed across multiple computers or processors or even the "cloud".

Storage media suitable 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 product may be distributed on such storage media or the download may be provided by HTTP, FTP, email or by a server connected to a network such as the internet.

Claims (15)

1. A brain control interface system (100), comprising:

a brain control interface (120) configured to detect brain signals indicative of brain activity of a user (160) in an environment (150),

An input (102) configured to obtain data indicative of a current light scene of one or more lighting devices (112, 114) in an environment (150),

-A lighting controller (110) configured to control the one or more lighting devices (112, 114), and

-One or more processors (106) configured to: the brain signal is analyzed to identify a noise level in the brain signal when the current light scene is active, and the light scene is adjusted while monitoring the noise level if the noise level exceeds a threshold until a target level of noise in the brain signal has been established.

2. The brain control interface system (100) of claim 1, wherein the light scene is a dynamic light scene that varies over time, wherein the dynamic light scene has a dynamic level, and wherein the one or more processors are configured to adjust the dynamic level until a target level of noise in the brain signal has been established.

3. The brain control interface system (100) of any preceding claim, wherein the one or more processors are configured to iteratively adjust the light scene while monitoring the noise level until a target level of noise in the brain signal has been established.

4. The brain control interface system (100) of any preceding claim, 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 has been established.

5. The brain control interface system (100) of claim 4, wherein the target light scene includes at least one of the following characteristics:

higher intensity compared to the light scene,

-A lower dynamic level compared to the light scene, and

-A spectrum comprising more blue light than the light scene.

-A spectrum comprising more red light than the light scene.

6. The brain control interface system (100) of any preceding claim, 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 wherein the one or more processors are further configured to switch to a brain control mode when a target level of noise in the brain signals has been established, wherein 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 control interface system (100) of claim 6, wherein the controllable device is a lighting device of the one or more lighting devices.

8. The brain control interface system (100) of claim 6 or 7, wherein the one or more processors are configured to select the target level of noise based on an expected control command to be provided by a user.

9. The brain control interface system (100) of any preceding claim, wherein the input is configured to obtain data indicative of the current light scene by obtaining sensor data from a light sensor.

10. The brain control interface system (100) according to any preceding claim, wherein the input is a receiver configured to receive data indicative of the current light scene from a lighting system controller.

11. The brain control interface system (100) according to any preceding claim, wherein the current light scene is provided by a plurality of lighting devices,

Wherein 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, and

Wherein the one or more processors are configured to: one or more of the plurality of lighting devices is selected based on a position and/or orientation of a user relative to the plurality of lighting devices, and the light scene is adjusted by adjusting a light output of the one or more selected lighting devices.

12. The brain control interface system (100) of any preceding claim, wherein the one or more processors are configured to initiate identifying the noise level and adjusting the light scene upon receiving a control input indicating that a user is to provide a control command.

13. The brain control interface system (100) of claim 12, wherein the control input is provided by a user, by a sensor, or by the brain control interface.

14. A method (500) of adjusting a light scene, the method comprising:

detecting (502) a brain signal indicative of brain activity of a user in an environment through a brain control interface,

Obtaining (504) data indicative of a current light scene of one or more lighting devices in the environment,

-Analyzing (506) an EEG signal to identify a noise level in the brain signal when the current light scene is active, and

-Adjusting (508) the light scene while monitoring the noise level if the noise level exceeds a threshold until a target level of noise in the brain signal has been established.

15. A computer program product for a computing device, the computer program product comprising computer program code to perform the method (500) of claim 14 when the computer program product is run on a processing unit of the computing device.