Overview

Figure below shows a photo of our mobile & wireless EEG system that incorporates novel dry MEMS electrodes that do not require any skin preparation or conductive pastes and miniaturized accurate steering in a realistic driving simulator (left center) battery-powered bioamps, filters, analog-to-digital converters and wireless telemetry circuits to enable imaging of participants actively performing ordinary tasks in natural body positions and situations in operational environments.

CLICK to watch a new video demo of a mobile & wireless EEG system. EEG signals were acquired by either dry EEG or commercially available EEG electrodes, miniture electronic circuits, and transmitted through Bluetooth telemetry to a cell phone. The cell phone is programed to to assess fluctuations in individuals' alertness and capacity for performing cognitive tasks based on the received EEG signals.

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1. Elliott, FS, and Makeig, S, "P300 amplitude covaries with error rate", Society for Psychophysiology, abstract 28:S20, 1991.
2. Bartlett, M, Makeig, S, Bell, A, Jung T-P, Sejnowski, T, "Independent Component Analysis of EEG Data", Society for Neuroscience Abstracts, 21:437, 1995.
3. Makeig, S, and Jung, T-P, "Fast (20 sec) and Slow (4+ min) Changes in Auditory Awareness", Proceeding of Proceeding of World Congress Sleep Research, Sep 1995.
4. Makeig, S, Jung T-P, Ghahremani, D, Bell, A, Sejnowski, TJ, "What (Not Where) are the Sources of the EEG?", , Proc. The 18th Annual Meeting of The Cognitive Science Society 802. 1996.
5. Makeig, S, Anllo-Vento, L, Jung T-P, Bell, A, Sejnowski, TJ, Hillyard SA, "Independent Component Analysis of event-related potentials during selective attention", Society for Neuroscience Abstracts, 22:1698, 1996.
6. Makeig, S, Jung, T-P, Bell, AJ, Ghahremani, D., and Sejnowski, TJ, "Blind separation of event-related brain response components", Society for Psychophysiological Research, Abstract, Oct. 1996.
7. Makeig, S, Anllo-Vento, L, Jung, T-P, Bell, AJ, Sejnowski, TJ, and Hillyard, SA, " Independent component analysis of event-related potentials during a selective attention task.", Society for Psychophysiological Research, Abstract, Oct. 1996.
8. McKeown MJ, Makeig, S., Jung, T-P, Brown, GG, Kindermann, SS, and Sejnowski, TJ, "Finding the Time Courses of Spatially Independent Activations in fMRI Data during STROOP and Control Tasks", Society for Neuroscience Abstracts, Oct. 1997.
9. Makeig, Jung T-P, and Sejnowski, "Independent Component Analysis of Single-trial Event-related Potentials", Society for Neuroscience Abstracts, Oct. 1997.
10. Van Orden K, Jung T-P, and Makeig S, "Eye Activity Correlates of Fatifue during a Visual Tracking Task," Meeting of Human Factor Society, 1998.

Articles

1. Makeig, S, Elliott, FS, Inlow, M, and Kobus, D "Predicting Lapses in Vigilance Using Brain Evoked Responses to Irrelevant Auditory Probes" , Technical Report 90-39, Naval Health Research Center, San Diego, CA, 1990.
2. Makeig, S and Inlow, M, "Lapses in alertness: coherence of fluctuations in performance and the EEG spectrum", Electroencephalogr clin Neurophysiol , 86:23-35, 1993.
3. Makeig, S, "Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones", Electroencephalogr clin Neurophysiol , 86:283-293, 1993.
4. Makeig, S, Elliott, FS, and Postal, M, First Demonstration of an Alertness Monitoring/Management System, Technical Report 93-36, Naval Health Research Center, San Diego, CA, 1993.
5. Jung, T-P and Makeig, S, "Estimating Level of Alertness from EEG", Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1103-4, 1994.
6. Jung, T-P and Makeig, S, "Monitoring Alertness Dynamics via Analysis of the EEG", Proceedings of the Annual Symposium of the Biomedical Engineering Society, 341-342, 1994.
7. Jung, T-P and Makeig, S, "Prediction Failures in Auditory Detection from Changes in the EEG Spectrum", Proceedings of the 17th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 927-928, 1995.
8. Makeig, S and Jung, T-P, "Changes in Alertness Are a Principal Component of Variance in the EEG Spectrum", NeuroReport, 7(1), 213-216, 1995.
9. Makeig, S, Bell, AJ, Jung, T-P, and Sejnowski, TJ, "Independent component analysis of Electroencephalographic data." Advances in Neural Information Processing Systems 8, 145-151,1996.
10. Makeig, S, Jung, T-P, and Sejnowski, TJ, "Using feedforward neural network to monitor alertness from changes in EEG correlation and coherence," Advances in Neural Information Processing Systems 8, 931-937, 1996.
11. Ghahremani D, Makeig, S, Jung T-P, Bell, A, Sejnowski, TJ, "Independent component analysis of simulated EEG using a three-shell spherical head model", Tech Rep. INC-9601, Institute for Neural Computation, University of California, San Diego, May 1996.
12. Makeig, S and Jung, T-P, "Tonic, phasic and transient EEG correlates of auditory awareness in drowsiness," Cogn Brain Res 4, 15-25, 1996.
13. Jung, T-P, Makeig, S, Stensmo, M, and Sejnowski, TJ, "Estimating alertness from the EEG power spectrum," IEEE Trans Biomed Eng, 44(1), 60-69, 1997.
14. Orden K, Jung T-P, and Makeig S, "Combined eye activity measures accurately estimate changes in sustained visual task performance," Biological Psychology, 52(3):221-40, 2000.
15. Makeig S, Enghoff S, Jung T-P, and Sejnowski TJ, "A Natural Basis for Efficient Brain-Actuated Control", IEEE Trans Rehab Eng, 8:208-11, 2000.
16. Makeig S, Jung T-P, and Sejnowski TJ, "Awareness during Drowsiness: Dynamics and Electrophysiological Correlates", Canadian J. Experimental Psychology, 54(4):266-73, 2000.
17. Van Orden K., Limbert W., Makeig S. and Jung T-P, "Eye Activity Correlates of Workload during a Visualspatial Memory Task," Human Factor, 43(1):111-21, 2001.
18. Huang, R-S. Jung, T-P and Makeig, S. Analyzing Event-Related Brain Dynamics in Continuous Compensatory Tracking Tasks , Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.
19. Liang, S.F., Lin, C-T, Wu, R.C, Chen, Y-C, Huang, T.Y. and Jung, T-P, Monitoring Driver's Alertness based on the Driving Performance Estimation and the EEG Power Spectrum , Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.
20. Lin, C-T., Wu, R-C, Jung, T-P., Liang, S-F, and Huang, T-Y. Estimating Driving Performance Based on EEG Spectrum Analysis , EURASIP Journal on Applied Signal Processing 19: 3165-74, 2005.
21. Lin, C-T., Wu, R-C, Liang, S-F, and Huang, T-Y. Chao, W-H. Chen, Y-J., Jung, T-P., EEG-based Drowsiness Estimation for Safety Driving Using Independent Component Analysis,, IEEE Transactions on Circuits and Systems, 52(12):2726-38, 2005.
22. Lin, C-T., Ko, L-W., Chung, I-F., Huang, T-Y., Chen, Y-C., Jung, T-P., and Liang, S-F, Adaptive EEG-based Alertness Estimation System by Using ICA-based Fuzzy Neural Networks,, IEEE Transactions on Circuits and Systems I, 53(11): 2469-76, 2006.
23. Huang, R-S, Jung, T-P., Delorme A, Makeig, S. Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking, NeuroImaging, 39:1896-1909, 2008.
24. Lin, C-T., Ko, L-W, Chiou, J-C, Duann, J-R., Chiu, T-W., Huang, R-S., Liang, S-F, Jung, T-P., A noninvasive prosthetic platform using mobile & wireless EEG, Proceedings of the IEEE, 96(7):1167-83, 2008.
25. Ko, L-W., Tsai, I-L., Yang, F-S., Chung, J-F., Lu, S-W., Jung, T-P., and Lin, C-T., "Real-Time Embedded EEG-Based Brain-Computer Interface," Lecture Notes in Computer Science, Advances in Neuro-Information Processing, 1038-45, 2009.
26. Chen Y-C, Duann J-R, Chuang S-W, Lin C-L, Ko, L-W, Jung T-P, Lin, C-T. Spatial and Temporal EEG Dynamics of Motion-sickness, NeuroImage, 49:2862-70, 2010.
27. Wang, Y., Wang, Y-T., Jung, T-P., Visual stimulus design for a high-rate SSVEP BCI, IET Electronics Letters, 46(15): 1057–8, 2010.
28. Wang, Y-T., Wang, Y., Jung, T-P., A Cell-Phone Based Brain-Computer Interface for Communication in Daily Life, Journal of Neural Engineering, in press.

Abstract

A total of ten male subjects (ages 18-34) participated in a simulation of a passive sonar auditory target detection task. Throughout each session, intermittent target and probe auditory stimuli were presented binaurally in the presence of a continuous white noise background at 62 dB nHL. Target noise bursts were presented at 6 dB SL and were 300 ms in duration with rise and fall times > 110 ms. Each subject participated in two simulated work sessions of 28 minutes, seated in a comfortable chair with eyes closed and pressing a button each time they detected a target noise burst. EEG and EOG signals were amplified 50K times with a 0.1-100 Hz bandwidth through Grass EEG amplifiers and converted continuously to 12-bit digital format at a sampling rate of 312.5 Hz per channel. EEG was collected from 13 central scalp locations of the International 10-20 system referred to the right mastoid. Periocular electrodes were used to record and reject from analysis electrical potentials generated by eye movements. A continuous estimate of performance at regularly spaced time intervals, local error rate, was derived by computing the fraction of undetected targets within a time window with a constant width of 32 s which was advanced through the data in 1.64 s steps. Targets were considered detected when target reaction times were within 200-2000 ms of stimulus onset. Because of technical problems, eighteen of the twenty sessions were used in the analysis. Brain evoked responses to detected targets (Hits) were ordered by increasing local error rate. A moving average of the reordered epochs at site Pz (center panel) reveals that as error rate increases above 50%, mean P300 amplitude decreases, while peak latency remains nearly constant. Examination of individual sessions showed that between-subject variability in P300 latency was not responsible for the decrease in amplitude of P300 to Hits. P300 latency remained near constant in nearly every run, even though mean reaction time in these experiments increased linearly by 256 ms from zero-error to highest-error rate epochs. Except for Lapses occurring when local error rate was very low, P300 is absent from "Lapse" target responses (right panel) and a triphasic response pattern appears. This response may be an equivalent of the "sleep P2-N2" that occurred in response to those task-irrelevant probes preceding target Lapses in this experiment. (Makeig et al, submitted). In the moving-averaged response to all targets (left panel), as local error rate increases, P300 amplitude declines and the triphasic Lapse response pattern emerges.We conclude that in task environment in which target time and response information is available, P300 amplitude may be used to predict current local error rate and hence, vigilance level. In situations in which such information is not available, other electrophysiological measures, including task-irrelevant probe responses and the EEG spectrum itself may be used to predict vigilance.

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Abstract

Because of the spread of electromagnetic signals through CSF and skull through volume conduction, EEG data recorded at different points on the scalp tend to be correlated. Bell and Sejnowski (1995) have recently presented an artificial neural network algorithm that identifies and separates statistically independent signals from a number of channels composed of linear mixtures of an equal number of sources. Here we present a first application of this Independent Component Analysis (ICA) algorithm to human EEG data. Conceptually, ICA filtering separates the problem of source identification in EEG data from the related problem of physical source localization. Three subjects performed a continuous auditory detection task in two half hour sessions. ICA filters trained on 14-channel EEG data collected during these sessions identified 14 statistically independent source channels which could then be further processed using event-related potential (ERP), event-related spectral perturbation (ERSP), and other signal processing techniques. One ICA source channel contained most eye movement activity, and another two collected line noise and muscle activity, while others were free of these artifacts. Changes in spectral power in several ICA channels covaried with changes in performance. If ICA sources can be shown to have distinct and consistent relationships to behavior or other physiological signals, ICA filtering may reveal meaningful aspects of event-related brain dynamics associated with sensory and cognitive processing but hidden within correlated EEG responses at individual scalp sites.

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Summary

Humans' ability to sustain their initial level of performance during continuous auditory or visual monitoring tasks is limited. After only a few minutes on task, particularly in low-arousal environments, performance of subjects in auditory or visual monitoring tasks often includes periods of intermittent failures to respond to above-threshold targets, alternating with periods of consistent responding. Previously, it has been noted that cycles of relatively good and poor performance tend to last four minutes or longer (Makeig and Inlow, 1993), and are experienced by subjects as waves of drowsiness. We show here that during drowsiness, human performance in detecting above-threshold auditory targets actually tends to vary irregularly on two time scales, 15-20 s cycles embedded in 4 min and longer cycles of intermittent performance.

Fifteen young adult volunteers participated in five half-hour sessions during which they pushed one button whenever they detected an above-threshold (10/min) auditory target stimulus (a brief increase in the level of continuous background noise). EEG was collected from two electrodes at the vertex and posterior midline, referred to the right mastoid. Thirty sessions from ten subjects, containing sufficient (mean +- s.d., 56+-26) response lapses were used in the analysis. Previously, minute-scale changes in the probability of detection in above-threshold signals during auditory vigilance tasks were shown to be accompanied by simultaneous shifts in spectral amplitude of several relatively narrow EEG frequency bands (Makeig and Inlow, 1993). We demonstrate here, using event-related spectral perturbations (ERSP) measure, that fast (20 sec) changes in auditory awareness also have well-defined EEG correlates.

In most subjects, EEG activity near 5 Hz begins to increase 10 s before undetected targets, while an increase in activity near 40 Hz precedes detected targets, these changes persisting on average until 10 s after target prsentation in parallel with local changes in detection probability. Response-related spectral perturbations at intermediate alpha and beta frequencies, on the other hand, begin after stimulus onset. Although mean activity at the posterior sleep spindle frequency (14 Hz) also increases during minute-scale periods intermittent detection, its second-to-second fluctuations do not covary with individual target detection outcome, implying that circa 5 Hz bursts, and not sleep spindles, are the EEG correlates of phasic lapses in auditory awareness during drowsiness.

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Abstract

The problem of determining brain electrical sources from potential patterns recorded on the scalp surface is mathematically underdetermined. We have applied the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski to the problem of source identification (What) considered apart from source localization (Where). By maximizing the joint entropy of a set of output channels derived from input signals by linear filtering without time delays, the ICA algorithm attempts to derive independent sources from highly correlated scalp EEG signals without regard to the locations or configurations (focal or diffuse) of the source generators. We report simulation experiments to determine (1) whether the ICA algorithm can successfully isolate independent components in simulated EEG generated by focal and distributed sources, and (2) whether ICA performance is severely affected by sensor noise and additional low-level brain noise sources. We will also show examples of ICA applied to actual EEG and cognitive ERP data.

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Abstract

Recordings of event-related potentials (ERPs) can reveal the time course of brain events associated with visual perception and selective attention. ERP studies of visual-spatial attention indicate that cortical processing of stimuli appearing in the attended location is augmented as early as 80 ms after stimulus onset. However, separation of the multiple brain processes contributing to the surface-recorded components of ERP waveforms has proven difficult. Recently, an `infomax' algorithm for the blind separation of linearly mixed inputs has been devised (Bell and Sejnowski, 1995) and applied to EEG and ERP analysis (Makeig et al., 1996). The neural generators of ICA sources are not specified by the algorithm and may be either physically compact or distributed.

Results of applying this Independent Component Analysis (ICA) algorithm to single-subject and group-mean ERPs recorded during a visual selective attention experiment (Anllo-Vento and Hillyard, 1996) suggest that ERP waveforms represent a sum of overlapping, discrete and time-limited brain processing events whose amplitudes are modulated by selective attention without affecting their time course. These source components identified by ICA appear to index independent stages of visual information processing. Spatial attention operates on early source components in a manner similar to a sensory gain-control mechanism, while later components appear to reflect further processing of stimulus features and feature conjunctions.

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Abstract

The problem of objectively decomposing event-related brain responses into neurophysiologically meaningful components is a major difficulty in the evoked response field. Traditional methods of identifying and measuring response subcomponents based on measuring the amplitudes and latencies of peak excursions in the waveforms at individual scalp sites fail when subcomponents overlap substantially, while current source localization procedures based on fitting single or multiple dipole models give ambiguous results when source geometry is unknown or complex. The Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (1995) is an artificial neural network which maximizes the overall entropy of a set of non-linearly transformed input vectors using stochastic gradient ascent, without regard to the physical locations or configuration of the source generators. Trained on one or more multichannel electric or magnetic evoked responses, the algorithm converges on spatial filters which separate the input data into independent time courses and distinct scalp topographies arising in multiple, spatially-stationary 'effective brain sources.' Response decompositions produced by the ICA algorithm can be used to measure the effects of experimental manipulations on individual response components, even when these are overlapping in time or space. Typically, response components identified by the algorithm are recaptured in repeated analyses, regardless of changes in initial weights, sensor montage, and data length. I will explain the theory and practise of ICA decomposition and its differences from PCA, demonstrate results of EEG simulations, and present applications to EEG and MEG data analysis.

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Abstract

Event-related potentials (ERPs) can reveal the time course of brain events associated with visual perception and selective attention. ERP studies of visual-spatial attention indicate that cortical processing of stimuli appearing in the attended location is augmented as early as 80 ms after stimulus onset. However, separation of the multiple brain processes contributing to the surface-recorded components of ERP waveforms has proven difficult. Recently, an `infomax' algorithm for the blind separation of linearly mixed inputs has been devised (Bell and Sejnowski, 1995) and applied to EEG and ERP analysis (Makeig et al., 1996). The neural generators of ICA sources are not specified by the algorithm and may be either physically compact or distributed. Results of applying this Independent Component Analysis (ICA) algorithm to single-subject and group-mean ERPs recorded during a visual selective attention experiment (Anllo-Vento and Hillyard, 1996) suggest that ERP waveforms represent a sum of overlapping, discrete and time-limited brain processing events whose amplitudes are modulated by selective attention without affecting their time course. These source components identified by ICA appear to index independent stages of visual information processing. Spatial attention operates on early source components in a manner similar to a sensory gain-control mechanism, while later components appear to reflect further processing of stimulus features and feature conjunctions.

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Abstract

Thirteen subjects participated in an auditory simulation of a passive sonar target detection environment. Targets were 300 ms noise bursts presented at near threshold levels in a noise background at a mean rate of 10 per minute. Task-irrelevant probe tones were also presented at inter-stimulus intervals of 2-4 seconds. Each subject participated in two 28 minute test sessions, pressing a button whenever they detected a noise target. Prominent minute-scale fluctuations in performance (computed as changes in local error rate using a 32-s moving window) occurred in many of the sessions. Evoked responses to the irrelevant probe tones in thirteen runs with highest number of performance lapses were sorted by current local error rate and smoothed using a moving-average. The amplitude of the grand mean N2 response to the irrelevant probe tones increased monotonically with error rate. Averaged evoked responses to relatively frequent, task-irrelevant probe tones appear to allow an accurate estimate of level of alertness if adequate numbers of trials are available.

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Abstract

Thirteen subjects detected noise burst targets presented in in a white noise background at a mean rate of 10 per minute. Within each session, local error rate, defined as the fraction of targets detected in a 33-second moving window, fluctuated widely. Mean coherence between slow mean variations in EEG power and in local error rate was computed for each EEG frequency and performance cycle length, and was shown by a Monte Carlo procedure to be significant for many EEG frequencies and performance cycle lengths, particularly in four well-defined EEG frequency bands, near 3, 10, 13, and 19 Hz, and at higher frequencies, in two cycle length ranges, one longer than 4 minutes and the other near 90 s per cycle. The coherence phase plane contained a prominent phase reversal near 6 Hz. Sorting individual spectra by local error rate confirmed the close relation between performance and EEG power and its relative within-subject stability. These results show that attempts to maintain alertness in an auditory detection task result in concurrent minute and multi-minute scale fluctuations in performance and the EEG power spectrum.

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Abstract

A first laboratory version of an Alertness Monitoring/Management (AMM) system has been designed and implemented. The system continually estimates the level of alertness of a human subject using EEG spectral information recorded from the subject's scalp, and delivers auditory feedback to assist the subject in managing his or her own level of alertness in work environments requiring constant vigilance. The system allows experimenters to monitor its input and output via real-time color graphics displays. As a first demonstration and evaluation of the system, six subjects participated in five half-hour sessions (three training and two feedback sessions), which involved dual detection tasks simulating the passive sonar environment. Auditory targets, 300-ms noisebursts presented at 6 dB above a noise background, were presented at a mean rate of 10 targets per minute. A continuous visual waterfall display presented illuminated vertical line targets at a mean rate of one per minute. Subjects pressed one response button to report noisebursts and another to report visual targets. Neural net estimation algorithms were trained for each subject to estimate the current probability of detecting auditory targets using electroencephalogram (EEG) and performance data collected during one or more of the initial training sessions. During feedback sessions, real-time signal processing and individualized neural network analysis of EEG recorded from a central scalp electrode were used to estimate continuously, in near real-time, the current probability-of-detection of auditory targets. Whenever this probability-of-detection measure declined below a preset threshold (e.g., when it predicted more than a 40% chance of failure to detect the auditory targets), the system sounded an alarm in the subject's headphones. When training sessions comprising a relatively wide range of detection rates were used to train the estimation algorithms, the alertness estimates followed changes in observed detection probability relatively accurately. Four of the six subjects reported that the alertness feedback helped them to maintain detection performance. A fifth subject did not produce enough detection lapses to fairly evaluate the system. Review of data from the sixth subject suggested that future versions of the system may be able to provide useful feedback to this subject as well. Review of results of the demonstration experiment have suggested several improvements to signal processing and training procedures used in the system. Effects of these enhancements on system performance are being evaluated.

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Abstract

A new measure of event-related brain dynamics, the event-related spectral perturbation (ERSP), is introduced to study event-related dynamics of the EEG spectrum induced by, but not phase-locked to, the onset of the auditory stimuli. The ERSP reveals aspects of event-related brain dynamics not contained in the ERP average of the same response epochs. Twenty-eight subjects participated in daily auditory evoked response experiments during a four day study of the effects of 24-hour free-field exposure to intermittent trains of 89 dB low frequency tones. During evoked response testing, the same tones were presented through headphones in random order at 5 s intervals. No significant changes in behavioral thresholds occurred during or after free-field exposure. ERSPs induced by target pips presented in some inter-tone intervals were larger than, but shared common features with ERSPs induced by the tones, most prominently a ridge of augmented EEG amplitude from 11 to 19 Hz, peaking 1-1.5 s after stimulus onset. Following 3-11 hours of free-field exposure, this feature was significantly smaller in tone-induced ERSPs; target-induced ERSPs were not similarly affected. These results therefore document systematic effects of exposure to intermittent tones on EEG brain dynamics even in the absence of changes in auditory thresholds.

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Abstract

Changes in EEG power collected at two sites from 10 subjects accompany slow and irregular fluctuations in alertness level. By merging power spectrum estimation, principal component analysis, and artificial neural networks, it is very feasible to accurately estimate shifts in an operator's level of alertness.

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Abstract

Real-time monitoring of alertness is highly desirable in a variety of operational environments where operators must sustain readiness during periods of low active decision-making. This paper demonstrates the feasibility of using physiological information, mainly the electroencephalogram (EEG), to accurately estimate, in real time, significant shifts in an operator's global level of alertness in a visual/auditory target detection task.

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Summary

During drowsiness, human performance in responding to above-threshold auditory targets tends to vary irregularly over periods of 4 minutes and longer. These performance fluctuations are accompanied by distinct changes in the frequency spectrum of the electroencephalogram (EEG) on three time scales: (1) During minute-scale and longer periods of intermittent responding, mean activity levels in the (< 4 Hz) delta and (4-6 Hz) theta bands, and at the sleep spindle frequency (14 Hz) are higher than during alert performance. (2) In most subjects, 4-6 Hz theta EEG activity begins to increase, and gamma band activity above 35 Hz begins to decrease, about 10 s before presentations of undetected targets, while before undetected targets, 4-6 Hz amplitude decreases and gamma band amplitude increases. Both these amplitude differences last 15-20 s and occur in parallel with event-related cycles in target detection probability. In the same periods, alpha and sleep-spindle frequency amplitudes also show prominent 15-20 s cycles, but these are not phase locked to performance cycles. (3) A second or longer after undetected targets, amplitude at intermediate (10-25 Hz) frequencies decreases briefly, while detected targets are followed by a transient amplitude increase in the same latency and frequency range.

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Abstract

Humans' ability to maintain constant level of performance in low-arousal task environments is limited. This paper shows that characteristic multi-minute and 15-20 sec fluctuations in the EEG spectrum accompanying fluctuations in behavioral alertness can be used to estimate or predict individual responses in an auditory detection task.

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Abstract

Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (Bell and Sejnowski, 1995) is suitable for performing blind source separation on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA analysis may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA analysis is capable of isolating overlapping alpha and theta wave bursts to separate ICA channels (4) Nonstationarities in EEG and behavioral state can be tracked using ICA analysis via changes in the amount of residual correlation between ICA-filtered output channels.

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Abstract

We report here that changes in the normalized electroencephalographic (EEG) cross-spectrum can be used in conjunction with feedforward neural networks to monitor changes in alertness of operators continuously and in near-real time. Previously, we have shown that EEG spectral amplitudes covary with changes in alertness as indexed by changes in behavioral error rate on an auditory detection task. Here, we report for the first time that increases in the frequency of detection errors in this task are also accompanied by patterns of increased and decreased spectral coherence in several frequency bands and EEG channel pairs. Relationships between EEG coherence and performance vary between subjects, but within subjects, their topographic and spectral profiles appear stable from session to session. Changes in alertness also covary with changes in correlations among EEG waveforms recorded at different scalp sites, and neural networks can also estimate alertness from correlation changes in spontaneous and unobtrusively-recorded EEG signals.

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Abstract

The Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (Bell and Sejnowski, 1995) is an information-theoretic unsupervised learning algorithm which can be applied to the problem of separating multichannel electroencephalographic (EEG) data into independent sources (Makeig et al., 1996). We tested the potential usefulness of the ICA algorithm for EEG source decomposition by applying the algorithm to simulated EEG data. This data was constructed by projecting known input signals from single- and multiple-dipole sources in a three-shell spherical model head (Dale and Sereno, 1993) to simulated scalp sensors.

In different simulations, we (1) altered the relative source strengths, (2) added multiple low-level sources (weak brain sources and sensor noise) to the simulated EEG, and (3) permuted the simulated dipole source locations and orientations. The algorithm successfully and reliably separated the activities of relatively strong sources from the activities of weaker brain sources and sensor noise, regardless of source locations and dipole orientations. These results suggest that the ICA algorithm should be able to separate temporally independent but spatially overlapping EEG activities arising from relatively strong brain and/or non-brain sources, irrespective of their spatial distributions.

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Abstract

We correlate minute-scale fluctuations in the normalized EEG log spectrum with concurrent changes in level of performance on a sustained auditory detection task, and show that a single principal component of EEG spectral variance is linearly related to minute-scale changes in detection performance. The particular EEG frequencies at which this coupling is expressed are similar for most subjects under a range of task conditions, and match those recently reported from analysis of verbal self-reports during drowsiness. The one-dimensional relationship between detection performance and the EEG spectrum confirms quantitatively the intuitive assumption that minute-scale changes in behavioral alertness during drowsiness are predominantly linked to changes in global brain dynamics along a single dimension of psychophysiological arousal.

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Abstract

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

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Abstract

Five concurrent eye activity measures were used to model fatigue-related changes in performance during a visual compensatory tracking task. Nine participants demonstrated considerable variations in performance level during two 53-min testing sessions in which continuous video-based eye activity measures were obtained. Using a trackball, participants were required to maneuver a target disk (destabilized by pseudorandom wind forces) within the center of an annulus on a CRT display. Mean tracking performance as a function of time across 18 sessions demonstrated a monotonic increase in error from 0 to 11 min, and a performance plateau thereafter. Individual performance fluctuated widely around this trend -- with an average root mean square (RMS) error of 2.3 disk radii. For each participant, moving estimates of blink duration and frequency, fixation dwell time and frequency, and mean pupil diameter were analyzed using non-linear regression and artificial neural network techniques. Individual models were derived using eye and performance data from one session and cross-validated on data from a second session run on a different day. A general regression model (based only on fixation dwell time and frequency) trained on data from both sessions from all participants produced a correlation of estimated to actual tracking performance of R=0.68 and an RMS error of 1.55 (S.D.=0.26) disk radii. Individual non-linear regression models containing a general linear model term produced the cross-session correlations of estimated to actual tracking performance of R=0.67. Individualized neural network models derived from the data of both experimental sessions produced the lowest RMS error (mean=1.23 disk radii, S.D.=0.13) and highest correlation (R=0.82) between eye activity-based estimates and actual tracking performance. Results suggest that information from multiple eye measures may be combined to produce accurate individualized real-time estimates of sub-minute scale performance changes during sustained tasks.

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Abstract

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central /spl mu/-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to the a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. The authors demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels, ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain actuated control in motor-limited and locked-in subjects.

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Abstract

Changes in six measures of eye activity were assessed as a function of task workload in a target identification memory task. Eleven participants completed four 2-hr blocks of a mock anti-air-warfare task, in which they were required to examine and remember target classifications (friend/enemy) for subsequent prosecution (fire upon/allow to pass), while targets moved steadily toward two centrally located ship icons. Target density served as the task workload variable; between one and nine targets were simultaneously present on the display. For each participant, moving estimates of blink frequency and duration, fixation frequency and dwell time, saccadic extent, and mean pupil diameter, integrated over periods of 10 to 20 s, demonstrated systematic changes as a function of target density. Nonlinear regression analyses found blink frequency, fixation frequency, and pupil diameter to be the most predictive variables relating eye activity to target density. Participant-specific artificial neural network models, developed through training on two or three sessions and subsequently tested on a different session from the same participant, correlated well with actual target density levels (mean R = 0.66). Results indicate that moving mean estimation and artificial neural network techniques enable information from multiple eye measures to be combined to produce reliable near-real-time indicators of workload in some visuospatial tasks. Potential applications include the monitoring of visual activity of system opetators for indications of visual workload and scanning efficiency.

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