WO2019226947A1

WO2019226947A1 - Systems and methods for enhanced wearable attention monitoring

Info

Publication number: WO2019226947A1
Application number: PCT/US2019/033841
Authority: WO
Inventors: Agatha LENARTOWICZ; Mark Steven COHEN
Original assignee: The Regents Of The University Of California
Priority date: 2018-05-25
Filing date: 2019-05-23
Publication date: 2019-11-28

Abstract

Systems and methods for monitoring a user's attention in accordance with embodiments of the invention are illustrated. One embodiment includes an attention monitoring device including a processor, a plurality of electroencephalography (EEG) electrodes positioned over the occipital scalp of a user, wherein the EEG electrodes are in communication with the processor, and a memory in communication with the processor, comprising an attention monitoring application, where the attention monitoring application directs the processor to obtain EEG data describing an EEG signal from the EEG electrodes, calculate a magnitude metric and a variability metric describing the EEG signal, where the magnitude metric reflects whether the user is focusing on an internal or external stimuli, and the variability metric reflects the degree of focus of the user.

Description

Systems and Methods for Enhanced Wearable Attention Monitoring

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The current application claims priority under 35 U.S.C. 119(e) to U.S. Provisional Patent Application Serial No. 62/676,575, entitled“Enhanced Wearable Attention Monitor”, filed May 25, 2018. The disclosure of U.S. Provisional Patent Application Serial No. 62/676,575 is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0002] The present invention is generally related to processing of EEG signals, and more specifically the monitoring of a user’s attentional integrity.

BACKGROUND

[0003] Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. EEGs can be performed noninvasively by placing electrodes in contact with a person’s head. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. Neural oscillations (“brain waves”) can be recorded using EEGs.

[0004] Alpha waves, during wakefulness, are neural oscillations in the frequency range of approximately 7.5-12.5 Hz arising from synchronous and coherent (in phase or constructive) electrical activity of thalamo-cortical cell interactions in humans. Alpha waves are one type of brain wave detected either by electroencephalography (EEG) or magnetoencephalography (MEG) and predominantly originate from the occipital lobe during wakeful relaxation with closed eyes. Alpha waves are reduced with open eyes, drowsiness and sleep.

SUMMARY OF THE INVENTION

[0005] Systems and methods for monitoring a user’s attention in accordance with embodiments of the invention are illustrated. One embodiment includes an attention monitoring device including a processor, a plurality of electroencephalography (EEG) electrodes positioned over the occipital scalp of a user, wherein the EEG electrodes are in communication with the processor, and a memory in communication with the processor, including an attention monitoring application, where the attention monitoring application directs the processor to obtain EEG data describing an EEG signal from the EEG electrodes, calculate a magnitude metric and a variability metric describing the EEG signal, where the magnitude metric reflects whether the user is focusing on an internal or external stimuli, and the variability metric reflects the degree of focus of the user.

[0006] In another embodiment, the EEG signal describes the alpha waves of the user.

[0007] In a further embodiment, the attention monitoring application further directs the processor to generate an attentional integrity score based on the magnitude metric and the variability metric, where the attentional integrity score reflects the overall quality of focus of the user.

[0008] In still another embodiment, the attention monitoring device further includes a display device.

[0009] In a still further embodiment, the display device is a cellular telephone.

[0010] In yet another embodiment, the attention monitoring application further directs the processor to display the magnitude metric and the variability metric via the display device.

[0011] In a yet further embodiment, the attention monitoring application further directs the processor to display the attentional integrity score via the display device

[0012] In another additional embodiment, to calculate the magnitude metric, the attention monitoring application further directs the processor to decompose the EEG signal by, sub-sampling the EEG signal using a first moving temporal window to produce a first set of temporal windows, and applying a spectral decomposition to each window in the first set of temporal windows to produce a time series of spectral magnitudes of the component frequencies of the EEG signal, select a plurality of spectral magnitudes from the time series of spectral magnitudes, where the selected spectral magnitudes are within a second moving temporal window of greater length than the first moving temporal window, and average the selected plurality of spectral magnitudes.

[0013] In a further additional embodiment, to calculate the variability metric, the attention monitoring application further directs the processor to calculate the standard deviation of the averaged spectral magnitude for a given moment of the second moving temporal window from the distribution of magnitudes of component frequencies across a plurality of windows in the first set of temporal windows that fall within the second moving temporal window.

[0014] In another embodiment again, the first moving temporal window is between 1 and 2 seconds long, and the second moving temporal window is one minute long.

[0015] In a further embodiment again, a method for monitoring the attention of a user, includes obtaining EEG data describing an EEG signal from a plurality of electroencephalography (EEG) electrodes positioned over the occipital scalp of the user, calculating a magnitude metric and a variability metric describing the EEG signal, where the magnitude metric reflects whether the user is focusing on an internal or external stimuli, and the variability metric reflects the degree of focus of the user.

[0016] In still yet another embodiment, the EEG signal describes the alpha waves of the user.

[0017] In a still yet further embodiment, the method further includes generating an attentional integrity score based on the magnitude metric and the variability metric, where the attentional integrity score reflects the overall quality of focus of the user.

[0018] In still another additional embodiment, the method further includes displaying information regarding the user’s attention using a display device.

[0019] In a still further additional embodiment, the display device is a cellular telephone.

[0020] In still another embodiment again, the method further includes displaying the magnitude metric and the variability metric via the display device.

[0021] In a still further embodiment again, the method further includes displaying the attentional integrity score via the display device

[0022] In yet another additional embodiment, calculating the magnitude metric further includes decomposing the EEG signal by sub-sampling the EEG signal using a first moving temporal window to produce a first set of temporal windows, and applying a spectral decomposition to each window in the first set of temporal windows to produce a time series of spectral magnitudes of the component frequencies of the EEG signal, selecting a plurality of spectral magnitudes from the time series of spectral magnitudes, where the selected spectral magnitudes are within a second moving temporal window of greater length than the first moving temporal window, and averaging the selected plurality of spectral magnitudes.

[0023] In a yet further additional embodiment, calculating the variability metric includes calculating the standard deviation of the averaged spectral magnitude for a given moment of the second moving temporal window from the distribution of magnitudes of component frequencies across a plurality of windows in the first set of temporal windows that fall within the second moving temporal window.

[0024] In yet another embodiment again, the first moving temporal window is between 1 and 2 seconds long, and the second moving temporal window is one minute long.

[0025] Additional embodiments and features are set forth in part in the description that follows, and in part will become apparent to those skilled in the art upon examination of the specification or may be learned by the practice of the invention. A further understanding of the nature and advantages of the present invention may be realized by reference to the remaining portions of the specification and the drawings, which forms a part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] FIG. 1 is a conceptual diagram illustrating an enhanced wearable attention monitoring system in accordance with an embodiment of the invention.

[0027] FIG. 2 is a conceptual diagram illustrating an enhanced wearable attention monitor in accordance with an embodiment of the invention.

[0028] FIG. 3 is a flow chart illustrating an attention monitoring process for generating attentional integrity metrics in accordance with an embodiment of the invention.

[0029] FIG. 4 is a chart illustrating different alpha wave patterns exemplary of different states of attentional integrity in accordance with an embodiment of the invention. DETAILED DESCRIPTION

[0030] Turning now to the drawings, systems and methods for enhanced wearable attention monitoring are illustrated. The ability to monitor the attention of an individual has wide reaching applications in numerous fields. For example, a supervisor could tell if workers handling heavy machinery were too distracted for the task; a clinician could monitor a child’s attention response to social cues, both in the clinic and remotely (when the child is under evaluation at home); a teacher could assess whether a teaching activity is effectively engaging a pupil with ADHD.

[0031] “Attention integrity” and“attentional integrity” describe the state of the attention system of the brain as it varies on a continuum from optimal functioning condition (high attention integrity) to corrupt and/or ineffective (low attention integrity). Attentional integrity metrics describe attentional integrity in a numerical fashion. However, attentional integrity metrics can be mapped to categorical, text variables to assist with user comprehension when desired. Attentional integrity metrics can further describe the direction of focus of an individual, or“orientation”, e.g. whether the individual focused on internally (without sensory inputs) or externally (with sensory inputs) generated signals. Internal thoughts generally include mind-wandering, creating thinking, remembering, and imagery, and are sometimes referred to as “spontaneous thought” and “undirected thought.” Broadly, internal focus is when attention is directed to cognitive activities that are not associated with a stimulus in the external environment, but are driven by internal processes such as internal thoughts.

[0032] One way to measure attentional integrity is through the monitoring and analysis of alpha waves. Alpha waves largely originate in the occipital lobe of the brain, and are tied closely to visual attention. In many embodiments, enhanced wearable attention monitors have recording electrodes placed over the occipital lobe to monitor alpha wave responses with increased signal-to-noise ratio (SNR) compared to that when electrodes are placed on other regions of the user’s head (e.g. over the frontal, temporal, or central scalp). Alpha waves can be recorded by enhanced attention integrity monitors and classified by attentional integrity metrics using attentional integrity monitoring processes. A variety of signal processing techniques can be applied to recorded alpha waves to generate attentional integrity metrics such as, but not limited to, magnitude metrics and variability metrics. Magnitude metrics can be used to identify whether or not a user is focusing on external stimuli or internal thought, enabling a user or observer to determine the direction of the user’s attention. Variability metrics can be used to identify whether or not the attention of a user is stable. Variability metrics generated by enhanced wearable attention monitoring systems can reflect a user’s unique brain activity and can provide insight into a user’s attentional state at a given time not available through the use of magnitude metrics alone.

[0033] In numerous embodiments, enhanced wearable attention monitors can provide users with scores or other indicators based on generated metrics reflecting whether the user is focused on external stimuli or internal thought, trends of the user on their attentional focus, and/or the strength of that focus. Profiles can be built for particular users by logging historical data and continuously updating a stored value of average magnitude and/or variability metrics. Systems for enhanced wearable attention monitors capable of recording and processing alpha waves to measure attentional integrity are described below.

Enhanced Wearable Attention Monitoring Systems

[0034] Enhanced wearable attention monitors can work in concert with other devices to enhance usability and/or provide information in more easily accessible ways. Turning now to FIG. 1 , a system diagram for an enhanced wearable attention monitoring system in accordance with an embodiment of the invention is illustrated.

[0035] The enhanced wearable attention monitoring system 100 includes an enhanced wearable attention monitor 1 10. Enhanced wearable attention monitors include a set of EEG electrodes that are capable of measuring EEG signals from a user’s brain in real time. In many embodiments, each electrode may be associated with a reference level. In some embodiments, reference levels are obtained via reference electrodes. In numerous embodiments, enhanced wearable attention monitors include a structure that holds the sensing electrodes against the back of a user’s head, over the occipital scalp. In a variety of embodiments, a feedback mechanism can be incorporated to verify the correct placement of the enhanced wearable attention monitor. For example, an audio and/or visual indicator can be provided to indicate when the signal-to-noise ratio is acceptable for use. In some embodiments, enhanced wearable attention monitors can be attached to separate supporting structures, such as, but not limited to, a hats, helmets, glasses, or any other structure capable of holding electrodes as appropriate to the requirements of specific applications of embodiments of the invention.

[0036] Enhanced wearable attention monitoring system 100 further includes interface devices 120 and 130. Interface devices can be any device that is capable of communicating with the enhanced wearable attention monitor such as, but not limited to, personal computers 120, smartphones 130, other computing devices, or any other device as appropriate to the requirements of specific applications of embodiments of the invention. Interface devices can communicate with enhanced wearable attention monitors via network 140. Network 140 can be any type of network, such as, but not limited to, the internet, a wide area network, a local area network (e.g. Wi-Fi), personal area networks (e.g. Bluetooth), or any other communication method as appropriate to the requirements of a given embodiment. Data from an enhanced wearable attention monitor can be processed and/or displayed using interface devices. As such, enhanced wearable attention monitors and/or interface devices can be capable of processing EEG data to produce magnitude metrics and variability metrics. Further, enhanced wearable attention monitors and/or interface devices can track and log individual EEG data and/or attentional integrity metrics in profile data describing a user’s brain activity over time. Enhanced wearable attention monitors in accordance with various embodiments of the invention are described in further detail below.

Enhanced Wearable Attention Monitors

[0037] Enhanced wearable attention monitors can collect EEG data by recording the brain waves of a user. In numerous embodiments, enhanced wearable attention monitors can process EEG data to calculate attentional integrity metrics. Turning now to FIG. 2, a conceptual diagram of an enhanced wearable attention monitor in accordance with an embodiment of the invention is illustrated. Enhanced wearable attention monitor 200 includes a processor 210. Processor 210 can be any logic circuitry capable of executing instructions such as, but not limited to, a microprocessor, a central processing unit, a graphics processing unit, an application-specific integrated circuit, a field-programmable gate array, or any other processing unit as appropriate to the requirements of specific applications of embodiments of the invention.

[0038] The enhanced wearable attention monitor 200 further includes an EEG electrode interface 220 capable of receiving EEG data from a set of two or more EEG electrodes and an input/output (I/O) interface capable of communicating data between Enhanced wearable attention monitor 200 and interface devices. In some embodiments, EEG interface 220 and I/O interface 230 are implemented using the same hardware.

[0039] The enhanced wearable attention monitor 200 includes a memory 240. Memory 240 can be volatile memory (e.g. random-access memory, etc.), non-volatile memory (flag memory, etc.), or any other type of memory as appropriate to the requirements of specific applications of embodiments of the invention. The contents of memory 240 includes an attention monitoring application 242. In many embodiments, attention monitoring applications can direct the processor to collect EEG data 244 based on recordings received from the EEG electrode interface. In numerous embodiments, attention monitoring applications can direct the processor to perform attention monitoring processes such as, but not limited to, those that produce attentional integrity metrics from EEG data. In a variety of embodiments, interface devices are capable of performing attention monitoring processes based on EEG data produced by the enhanced wearable attention monitor. In this way, processing can be split between enhanced wearable attention monitors and interface devices. While specific architectures for enhanced wearable attention monitors are described above, any number of alternative architectures, including those that replace, add, or remove components can be utilized as appropriate to the requirements of specific applications of embodiments of the invention. Further, while attention monitoring processes are discussed below with respect to being performed by an enhanced wearable attention monitor, similar processes can be performed by interface devices with similar results.

Attention Monitoring Processes

[0040] In many embodiments, attention monitoring processes are performed by enhanced attention monitoring systems to produce attentional integrity metrics. Attention monitoring processes can be performed by enhanced wearable attention monitors, interface devices, or a combination of devices within the system. Turning now to FIG. 3, an attention monitoring process for generating attentional integrity metrics based on EEG data in accordance with an embodiment of the invention is illustrated.

[0041] Process 300 includes obtaining (310) EEG data. In numerous embodiments, EEG data are obtained by enhanced wearable attention monitors. In a variety of embodiments, EEG data describes the alpha wave signal from a user’s occipital lobe. In many embodiments, the EEG data is obtained via the EEG electrodes as an analog signal. The analog signal can be converted into a digital signal using a digital-to-analog converter (ADC). Exemplary alpha wave signals in accordance with an embodiment of the invention are illustrated in FIG. 4. The EEG signal described by the EEG data is decomposed (320) into their spectral components using any of a number of transforms including but not limited to Fourier or wavelet transforms. Metrics describing the magnitude and variability of the EEG signal and its spectral components can be determined (330), and the magnitude and variability metrics can be normalized (340). In numerous embodiments, the metrics are normalized by calculating a moving average running mean.

[0042] In numerous embodiments, signal decomposition is achieved by applying a Fourier transform, such as, but not limited to, a fast Fourier transform (FFT), and/or equivalent techniques that decompose a time signal into its frequency components. Flowever, any number of signal processing techniques can be applied to decompose an EEG signal in accordance with the requirements of a given application. In many embodiments, the decomposition is performed by means of a moving window. For example, a temporal window of data can be sub-sampled from the time series described by the EEG data at the onset of recording. In many embodiments, the sub-sampling window is on the order of seconds. For example, in numerous embodiments, the sub- sampling window is between 1 and 2 seconds. Flowever, any length of time can be used as appropriate to the requirements of specific applications of embodiments of the invention. The resulting time series of windows can be used in further processing steps. For example, within the window the FFT can be applied and the magnitude of the frequency components in the alpha wave range can be obtained and averaged across frequencies in the range. The resulting magnitude value can be stored, and the temporal window can be shifted forward in time and the process repeated until all time points have been considered. The result of this process is a time series of spectral magnitudes across time in the alpha range rather than the raw voltage values obtained by the EEG electrodes. In many embodiments, magnitude values are described as“power” (e.g., the square of the magnitude) or a transformed value of magnitude or“power” (e.g. the log of the value). Further, the above processing steps can be applied to real-time recording by defining the temporal window with respect to the start time of the recording and adaptively shifting the window with real time.

[0043] Attentional integrity metrics, such as magnitude and/or variability metrics can be generated (350). In many embodiments, the time series of spectral magnitudes is used to generate magnitude and/or variability metrics. A window of a desired length (on the order of tens of seconds) can be selected from the time series of spectral magnitudes. In numerous embodiments, the windows are on the order of minutes. For example, in many embodiments, one minute windows are used. Flowever, any length of window can be used as appropriate to the requirements of specific applications of embodiments of the invention. Within this window, the magnitude values can be averaged to generate magnitude metrics for that window. In numerous embodiments, the average value can be the mean, the median, and/or any measure of central tendency as appropriate to the requirements of specific applications of embodiments of the invention. The magnitude values can also be used to generate a variability statistic (e.g., variance, standard deviation) to generate variability metrics. In numerous embodiments, the magnitude and variability metrics are normalized by a stored value (e.g., relative magnitude change and coefficient of variation, respectively). The stored value can be proportional to the mean of the signal across past windows of recorded data, such as recorded data from the user during previous sessions and/or the current session, so that the magnitude and variability metrics describe the magnitude and stability of the signal with respect to the individual user rather than an average population value. Additionally, stored values representative of the average population can be used as well, or as a replacement for, individualized stored values.

[0044] In many embodiments, attentional integrity metrics include attentional integrity scores based on at least the magnitude and variability metrics. For example, an overall score of the user’s intentional integrity, or a metric describing whether or not the user is paying attention to external stimuli or internal thoughts. In numerous embodiments, magnitude metrics are indicative of internal or external attention (e.g. higher magnitude metrics indicate internal focus), and variability metrics indicate attentional integrity (e.g. lower variability indicates higher focus).

[0045] If process 300 is being applied in real-time while the user is still using the enhanced wearable attention monitor, the process can be continued until the device is no longer in use so that attentional integrity metrics can be updated continuously in near real-time. Attentional metrics can be provided to a user via a feedback mechanism. Such feedback can be integrated into the enhanced wearable attention monitor or an interface device. In numerous embodiments, displays are visual displays, however audio speakers, and/or tactile feedback devices (e.g. vibration generators) can be used in conjunction with, or as a replacement for visual displays.

[0046] Although the present invention has been described in certain specific aspects, many additional modifications and variations would be apparent to those skilled in the art. In particular, any of the various processes described above can be performed in alternative sequences in order to achieve similar results in a manner that is more appropriate to the requirements of a specific application. It is therefore to be understood that the present invention can be practiced otherwise than specifically described without departing from the scope and spirit of the present invention. Thus, embodiments of the present invention should be considered in all respects as illustrative and not restrictive.

Claims

WHAT IS CLAIMED IS:

1 . An attention monitoring device comprising:

a processor;

a plurality of electroencephalography (EEG) electrodes positioned over the occipital scalp of a user, wherein the EEG electrodes are in communication with the processor; and

a memory in communication with the processor, comprising an attention monitoring application, where the attention monitoring application directs the processor to:

obtain EEG data describing an EEG signal from the EEG electrodes; calculate a magnitude metric and a variability metric describing the EEG signal, where the magnitude metric reflects whether the user is focusing on an internal or external stimuli, and the variability metric reflects the degree of focus of the user.

2. The attention monitoring device of claim 1 , wherein the EEG signal describes the alpha waves of the user.

3. The attention monitoring device of claim 1 , wherein the attention monitoring application further directs the processor to generate an attentional integrity score based on the magnitude metric and the variability metric, where the attentional integrity score reflects the overall quality of focus of the user.

4. The attention monitoring device of claim 1 , wherein the attention monitoring device further comprises a display device.

5. The attention monitoring device of claim 4, wherein the display device is a cellular telephone.

6. The attention monitoring device of claim 4, the attention monitoring application further directs the processor to display the magnitude metric and the variability metric via the display device.

7. The attention monitoring device of claim 4, wherein the attention monitoring application further directs the processor to display the attentional integrity score via the display device.

8. The attention monitoring device of claim 1 , wherein to calculate the magnitude metric, the attention monitoring application further directs the processor to:

decompose the EEG signal by:

sub-sampling the EEG signal using a first moving temporal window to produce a first set of temporal windows; and

applying a spectral decomposition to each window in the first set of temporal windows to produce a time series of spectral magnitudes of the component frequencies of the EEG signal;

select a plurality of spectral magnitudes from the time series of spectral magnitudes, where the selected spectral magnitudes are within a second moving temporal window of greater length than the first moving temporal window; and average the selected plurality of spectral magnitudes.

9. The attention monitoring device of claim 8, wherein to calculate the variability metric, the attention monitoring application further directs the processor to calculate the standard deviation of the averaged spectral magnitude for a given moment of the second moving temporal window from the distribution of magnitudes of component frequencies across a plurality of windows in the first set of temporal windows that fall within the second moving temporal window.

10. The attention monitoring device of claim 8, wherein the first moving temporal window is between 1 and 2 seconds long, and the second moving temporal window is one minute long.

11. A method for monitoring the attention of a user, comprising:

obtaining EEG data describing an EEG signal from a plurality of electroencephalography (EEG) electrodes positioned over the occipital scalp of the user;

calculating a magnitude metric and a variability metric describing the EEG signal, where the magnitude metric reflects whether the user is focusing on an internal or external stimuli, and the variability metric reflects the degree of focus of the user.

12. The method of attention monitoring of claim 11 , wherein the EEG signal describes the alpha waves of the user.

13. The method of attention monitoring of claim 11 , further comprising generating an attentional integrity score based on the magnitude metric and the variability metric, where the attentional integrity score reflects the overall quality of focus of the user.

14. The method of attention monitoring of claim 11 , further comprising displaying information regarding the user’s attention using a display device.

15. The method of attention monitoring of claim 14, wherein the display device is a cellular telephone.

16. The method of attention monitoring of claim 14, further comprising displaying the magnitude metric and the variability metric via the display device.

17. The method of attention monitoring of claim 14, further comprising displaying the attentional integrity score via the display device.

18. The method of attention monitoring of claim 11 , wherein calculating the magnitude metric further comprises:

decomposing the EEG signal by:

selecting a plurality of spectral magnitudes from the time series of spectral magnitudes, where the selected spectral magnitudes are within a second moving temporal window of greater length than the first moving temporal window; and averaging the selected plurality of spectral magnitudes.

19. The method of attention monitoring of claim 18, wherein calculating the variability metric comprises calculating the standard deviation of the averaged spectral magnitude for a given moment of the second moving temporal window from the distribution of magnitudes of component frequencies across a plurality of windows in the first set of temporal windows that fall within the second moving temporal window.

20. The method of attention monitoring of claim 18, wherein the first moving temporal window is between 1 and 2 seconds long, and the second moving temporal window is one minute long.