Ultra-slow frequency bands reflecting potential coherence between neocortical brain regions.

Recent studies of electromagnetic ultra-slow waves (⩽0.1Hz) have suggested that they play a role in the integration of otherwise disassociated brain regions supporting vital functions (Ackermann and Borbely, 1997; Picchioni et al., 2010; Knyazev, 2012; Le Bon et al., 2012). We emphasize this spectral domain in probing sensor coherence issues raised by these studies using Hilbert phase coherences in the human MEG. In addition, we ask: will temporal-spatial phase coherence in regional brain oscillations obtained from the ultraslow spectral bands of multi-channel magnetoencephalograms (MEG) differentiate resting, "task-free" MEG records of normal control and schizophrenic subjects? The goal of the study is a comparison of the relative persistence of intra-regional phase locking values (PLVs), among 10, region-defined, sensors in examined in the resting multichannel, MEG records as a function of spectral frequency bands and diagnostic category. The following comparison of Hilbert-transform-engendered relative phases of each designated spectral band was made using their pair-wise PLVs. This indicated the proportion of shared cycle time in which the phase relations between the index location and reference leads were maintained. Leave one out, bootstrapping of the PLVs via a support vector machine (SVM), classified clinical status with 97.3% accuracy. It was generally the case that spectral bands ⩽1.0Hz generated the highest values of the PLVs and discriminated best between control and patient populations. We conclude that PLV analysis of the oscillatory patterns of MEG recordings in the ultraslow frequency bands suggest their functional significance in intra-regional signal coherence and provide a higher rate of classification of patients and normal subjects than the other spectral domains examined.

Intrinsic mode functions locate implicit turbulent attractors in time in frontal lobe MEG recordings.

In seeking evidence for the presence and characteristic range of coupled time scale(s) of putative implicit turbulent attractors of dorsal frontal lobe magnetic fields, the recorded nonstationary, nonlinear MEG signals were non-orthogonally decomposed using Huang's Empirical Mode Decomposition, EMD, (Huang and Attoh-Okine, 2005) into 16 Intrinsic Mode Functions, EMD→IMFi, i=1…16. Measures known to be invariant in non-uniformly hyperbolic (turbulent) dynamical systems, topological entropy, hT, metric entropy, hM, non-uniform entropy, hU and power spectral scaling exponent, α, were imposed on each of the IMFi which evidenced most clearly an invariant temporal scale zone of IMFi, i=6…11, for hT, which we have found to be the most robust of invariant measures of MEG's magnetic field turbulent attractors (Mandell et al., 2011a,b; Mandell, 2013). The ergodic theory of dynamical systems (Walters, 1982; Pollicott and Yuri, 1998) allows the inference that an implicit attractor with consistently hT>0 will also evidence at least one positive Lyapounov exponent indicating the presence of a turbulent attractor with exponential separation of nearby initial conditions, exponential convergence of distant points and disordering, mixing, of orbital sequences. It appears that this approach permits the inference of the presence of chaotic, turbulent attractor and its characteristic time scales without the invocation of arbitrary n-dimensional embedding, phase space reconstructions or (inappropriate) orthogonal decompositions.

Monitoring driver's alertness based on the driving performance estimation and the EEG power spectrum analysis.

Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver's abilities of perception, recognition and vehicle control abilities. This paper proposes methods for drowsiness estimation that combine the electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Results show that it is feasible to quantitatively monitor driver's alertness with concurrent changes in driving performance in a realistic driving simulator.

Dynamic brain sources of visual evoked responses.

It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a human visual selective attention task, we show that nontarget event-related potentials were mainly generated by partial stimulus-induced phase resetting of multiple electroencephalographic processes. Independent component analysis applied to the single-trial data identified at least eight classes of contributing components, including those producing central and lateral posterior alpha, left and right mu, and frontal midline theta rhythms. Scalp topographies of these components were consistent with their generation in compact cortical domains.

Analysis and visualization of single-trial event-related potentials.

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data.

Eye activity correlates of workload during a visuospatial memory task.

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.

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects.

OBJECTIVES: Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently. METHODS: Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations. RESULTS: Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods. CONCLUSIONS: The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.

A natural basis for efficient brain-actuated control.

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 mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be 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. We 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.

Removing electroencephalographic artifacts by blind source separation.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

Combined eye activity measures accurately estimate changes in sustained visual task performance.

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.

Awareness during drowsiness: dynamics and electrophysiological correlates.

During drowsy periods, performance on tasks requiring continuous attention becomes intermittent. Previously, we have reported that during drowsy periods of intermittent performance, 7 of 10 participants performing an auditory detection task exhibited episodes of non-responding lasting about 18 s (Makeig & Jung, 1996). Further, the time patterns of these episodes were repeated precisely in subsequent sessions. The 18-s cycles were accompanied by counterbalanced power changes within two frequency bands in the vertex EEG (near 4 Hz and circa 40 Hz). In the present experiment, performance patterns and concurrent EEG spectra were examined in four participants performing a continuous visuomotor compensatory tracking task in 15-20 minute bouts during a 42-hour sleep deprivation study. During periods of good performance, participants made compensatory trackball movements about twice per second, attempting to keep a target disk near a central ring. Autocorrelations of time series representing the distance of the target disk from the ring centre showed that during periods of poor performance marked near-18-s cycles in performance again appeared. There were phases of poor or absent performance accompanied by an increase in EEG power that was largest at 3-4 Hz. These studies show that in drowsy humans, opening and closing of the gates of behavioural awareness is marked not by the appearance of (12-14 Hz) sleep spindles, but by prominent EEG amplitude changes in the low theta band. Further, both EEG and behavioural changes during drowsiness often exhibit stereotyped 18-s cycles.

Functionally independent components of early event-related potentials in a visual spatial attention task.

Spatial visual attention modulates the first negative-going deflection in the human averaged event-related potential (ERP) in response to visual target and non-target stimuli (the N1 complex). Here we demonstrate a decomposition of N1 into functionally independent subcomponents with functionally distinct relations to task and stimulus conditions. ERPs were collected from 20 subjects in response to visual target and non-target stimuli presented at five attended and non-attended screen locations. Independent component analysis, a new method for blind source separation, was trained simultaneously on 500 ms grand average responses from all 25 stimulus-attention conditions and decomposed the non-target N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetrical component pair (N1aR and N1aL) was evoked by the left and right visual field stimuli, respectively. Component N1aR peaked ca. 9 ms earlier than N1aL. Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. The amplitudes of these components were no reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatio-temporally overlapping bilateral component (N1b) that peaked at ca. 180 ms and was strongly enhanced by attention. Stimuli presented at unattended locations evoked a fourth component (P2a) peaking near 240 ms. A fifth component (P3f) was evoked only by targets presented in either visual field. The distinct response patterns of these components across the array of stimulus and attention conditions suggest that they reflect activity in functionally independent brain systems involved in processing attended and unattended visuospatial events.

Functionally independent components of the late positive event-related potential during visual spatial attention.

Human event-related potentials (ERPs) were recorded from 10 subjects presented with visual target and nontarget stimuli at five screen locations and responding to targets presented at one of the locations. The late positive response complexes of 25-75 ERP average waveforms from the two task conditions were simultaneously analyzed with Independent Component Analysis, a new computational method for blindly separating linearly mixed signals. Three spatially fixed, temporally independent, behaviorally relevant, and physiologically plausible components were identified without reference to peaks in single-channel waveforms. A novel frontoparietal component (P3f) began at approximately 140 msec and peaked, in faster responders, at the onset of the motor command. The scalp distribution of P3f appeared consistent with brain regions activated during spatial orienting in functional imaging experiments. A longer-latency large component (P3b), positive over parietal cortex, was followed by a postmotor potential (Pmp) component that peaked 200 msec after the button press and reversed polarity near the central sulcus. A fourth component associated with a left frontocentral nontarget positivity (Pnt) was evoked primarily by target-like distractors presented in the attended location. When no distractors were presented, responses of five faster-responding subjects contained largest P3f and smallest Pmp components; when distractors were included, a Pmp component appeared only in responses of the five slower-responding subjects. Direct relationships between component amplitudes, latencies, and behavioral responses, plus similarities between component scalp distributions and regional activations reported in functional brain imaging experiments suggest that P3f, Pmp, and Pnt measure the time course and strength of functionally distinct brain processes.

Analysis of fMRI data by blind separation into independent spatial components.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Spatially independent activity patterns in functional MRI data during the stroop color-naming task.

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

Blind separation of auditory event-related brain responses into independent components.

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Estimating alertness from the EEG power spectrum.

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.

Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness.

During drowsiness, human performance in responding to above-threshold auditory targets tends to vary irregularly over periods of 4 min 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 detected 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.

Changes in alertness are a principal component of variance in the EEG spectrum.

Minute-scale fluctuations in the normalized EEG log spectrum, when correlated with concurrent changes in level of performance on a sustained auditory detection task, showed 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.