Scott Makeig
Selected Abstracts

Makeig, S., Westerfield, M., Townsend, J., Jung, T-P, Courchesne, E. and Sejnowski, T. J., FUNCTIONALLY INDEPENDENT COMPONENTS OF EARLY EVENT-RELATED POTENTIALS IN A VISUAL SPATIAL ATTENTION TASK, Philosophical Transactions of the Royal Society: Biological Sciences, 354:1135-44, 1999.

Spatial visual attention modulates the first negative-going deflection in the human averaged event-related potential (ERP) in response to visual target and nontarget 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 nontarget stimuli presented at five attended and non-attended screen locations. Independent Component Analysis (ICA), a new method for blind source separation, was trained simultaneously on half-sec grand average responses from all 25 stimulus/attention conditions and decomposed the nontarget N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetric component pair (N1aL and N1aR) was evoked by left and right visual field stimuli respectively. Component N1aR peaked ~9 msec earlier than N1aL. Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. Amplitudes of these components were not reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatiotemporally overlapping bilateral component (N1b) that peaked at around 180 msec and was strongly enhanced by attention. Stimuli presented in unattended locations only evoked a fourth component (P2a) peaking near 240 msec. 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.

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Makeig, S., Westerfield, M., Jung, T-P., Covington, J., Townsend, J., Sejnowski, T. J. and Courchesne, E. FUNCTIONALLY INDEPENDENT COMPONENTS OF THE LATE POSITIVE EVENT-RELATED POTENTIAL DURING VISUAL SPATIAL ATTENTION The Journal of Neuroscience 19:2665-2680, 1999.

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 to 75 ERP average waveforms from the two task conditions were simultaneously analyzed with Independent Component Analysis (ICA), 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 fronto-parietal component (P3f) began at around 140 ms 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 post-motor potential (Pmp) component that peaked 200 ms after the button press and reversed polarity near the central sulcus. A fourth component associated with a left fronto-central positivity (Pnt) was evoked primarily by target-like distractors presented in the attended location. When no distractors were presented, responses of 5 faster-responding subjects contained largest P3f and smallest Pmp components; when distractors were included, a Pmp component appeared only in responses of the 5 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.

Research supported by the Office of Naval Research, the NIH, the Swartz Foundation, and the Howard Hughes Medical Institute.

S. Makeig, T-P Jung and T.J. Sejnowski.
Society for Neuroscience Abstracts, 1998.

Single-trial multi-channel EEG epochs were analyzed using Independent Component Analysis (ICA), which decomposed data into a sum of temporally independent components projecting to spatially fixed scalp maps (Makeig et al., Proc Nat Acad Sci USA 94:10979-10984, 1997). ICA uses higher-order statistics to identify spatially coherent patterns of activation in the input data. Decomposing single-trial ERP epochs from a visual selective attention task with ICA revealed the scalp patterns and time courses of activation of multiple alpha, beta, and gamma band oscillatory EEG components which were consistently but differentially affected by cognitive task events. Event-related modulations included amplitude blocking or augmentation and slow or fast phase resetting time locked to target-stimulus onsets or motor responses. At alpha band frequencies (8-12 Hz), multiple components with differing scalp distributions were found in single subjects. Some beta band (16-24 Hz) components were also active in the alpha band, while others were not. These oscillatory ICA components may be major sources of inter-channel coherences measured in EEG coherence studies.

Research supported by the Office of Naval Research, the Swartz Foundation and the Howard Hughes Medical Institute.

M. Westerfield, J. Townsend, J. Covington, S. Makeig, T.J. Sejnowski & E. Courchesne.
Society for Neuroscience Abstracts, 1998.

The late positive event-related potential (ERP), a complex response dominated by the P300, reflects cognitive processes including attention. Although it has been shown that the late positive ERP is not a unitary response, overlapping spatial and temporal characteristics make the separation of contributing subcomponents difficult. Independent Components Analysis (ICA) can decompose complex ERP data into temporally independent and spatially fixed components. When applied to data recorded from normal subjects during a visual attention task, ICA revealed 3 independent components of the response to target stimuli: an early onset frontal positivity, a large parietal positivity, and a post-motor positivity that peaked approximately 200 ms following the button press and reversed polarity near the vertex. A fourth, frontally maximal component was elicited by 'no-go' stimuli.

Traditional analyses of late positive ERP data recorded from subjects with autism revealed differences from normal control data in both amplitude and latency, despite no differences in accuracy of task performance. ICA decomposition of these data suggests an explanation of these differences. While analogs of the subcomponents present in the normal data were also apparent in the autism data, the proportional contribution to the overall waveform was substantially different between the two groups. This may reflect differential use of the brain systems involved in the attentional processes utilized in this task, or differences in the distribution of visual attention resources.

Supported by NINDS 1RO1-NS34155, NIMH 1RO1-MH36840, the Office of Naval Research, The Swartz Foundation & the Howard Hughes Medical Institute.

T-P Jung, S. Makeig and T.J. Sejnowski.
Society for Neuroscience Abstracts, 1998.

Event-related potentials (ERPs), the portions of EEG signals that are both time- and phase-locked to some experimental events, are usually averaged to increase their signal/noise ratio relative to non-phase locked EEG activity regardless of the fact that in single stimulus epochs response activity may vary widely in both time course and scalp distribution. This study proposes a new visualization method (ERP-image) for investigating the latencies and amplitudes variability of event-evoked responses in spontaneous EEG by sorting single-trial ERP epochs in order of a relevant performance measure (e.g. reaction time) and plotting the potentials in a 2-D space. This method makes visible the single-trial contributions to averaged ERPs and can clearly display relationships between phase, amplitude and timing of ERP components and performance. The study employs a new linear decomposition method, Independent Component Analysis (ICA), to single-trial ERPs recorded at multiple scalp electrodes to derive spatial filters that decompose complex EEG data into a sum of temporally independent components arising from spatially fixed brain areas, networks, or neural populations. We have explored applications of ERP-image and ICA decomposition to single-trial ERPs in a visual selective attention task involving, (1) removing eye and muscle artifacts that interfere with EEG analysis, while preserving the underlying brain activity in the EEG; (2) extracting event-related responses from spontaneous EEG; (3) identifying spatially-overlapping patterns of coherent activity rather than focusing on single scalp channels or channel pairs as in all current analysis methods; (4) separating oscillatory EEG activity into several components with distinct frequency contents.

Research supported by the Howard Hughes Medical Institute, the Swartz Foundation and the Office of Naval Research.

Scott Makeig, Tzyy-Ping Jung, Terrence J. Sejnowski.
Society for Psychophysiological Research Abstracts, 1998.

Independent Component Analysis (ICA) is an emerging signal processing technique that can decompose either spontaneous or evoked electrophysiological data into spatially fixed and temporally independent components. ICA derives both the scalp distributions and time courses of independent components whose linear mixtures form its input data, Input data may be averaged or unaveraged single-trial epochs time locked to experimental events, or continuous recordings of spontaneous activity. Applied to spontaneous data or collections of single trial epochs, ICA can identify components arising from movement, muscle, or line noise, as well as from electrocardiographic and other artifacts. These can then be removed from the data without sacrificing underlying EEG or MEG activity at any of the channels. Applied to 75 grand-average ERPs from a visual selective attention task, ICA identified four major components of late positive complex in 'go' and 'nogo' stimulus responses (Makeig et al., SPR, 1997). Several details of the time courses of individual components proved systematically related to response time, task conditions and subject ability. Decomposition of single trials from these same experiments identified multiple oscillatory components at a range of frequencies. These components reacted distinctly differently to experimental events. Event-related modulations of oscillatory ICA components may parsimoniously account for event-related changes in spectral amplitudes at single channels and also for event-related changes in channel-pair coherences.

Supported by the Office of Naval Research, the Swartz Foundation and the Howard Hughes Medical Institute.

Scott Makeig, Tzyy-Ping Jung, Te-Won Lee and Terrence J. Sejnowski
Society for Psychophysiological Research Abstracts, 1997.

Independent Component Analysis (ICA) is a new signal processing technique for decomposing spontaneous or evoked EEG and MEG data into temporally independent and spatially fixed components. The scalp distribution of the auditory steady-state response near 40 Hz appears to sweep from the front to the back of the scalp every cycle. ICA decomposes this apparent movement into the sum of at least two bilateral components with different scalp distributions and phase lags. ICA accounts for the transient perturbations in SSRs produced by experimental events using the same components producing the SSR, supporting the hypothesis that these transient (CERP) perturbations represent modulation of the ongoing response. Application of ICA algorithms capable of both sub-Gaussian and super-Gaussian components will be presented and psychophysiological implications of new blind decomposition techniques discussed.

Scott Makeig, Marissa N. Westerfield, Jeanne Townsend, James W. Covington, Eric Courchesne and Terrence J. Sejnowski.
Society for Psychophysiological Research Abstracts, 1997.

Independent Component Analysis (ICA) is a new signal processing technique for decomposing spontaneous or evoked electrophysiological data into spatially fixed and temporally independent components. ICA allows comparison of component amplitudes and time courses across related conditions. Applied simultaneously to target and nontarget responses in 30 conditions of a visual selective attention experiment (see Westerfield et al., this session), ICA derived at least four components of the early visual evoked response which were differently amplitude-modulated by spatial location and attention without effects on component latency, but were not affected by the target/nontarget distinction. Other components accounted for data artifacts in single conditions. Later components common to several conditions were sensitive to both spatial attention and target feature. ICA allows quantitative comparison of objectively-derived and temporally-sparse ERP components and subcomponents across 30 or more stimulus or task conditions.

Supported by the Office of Naval Research, Howard Hughes Medical Institute, NINDS NS34155 and NIMH MH36840.

S. Makeig, T-P. Jung and T. J. Sejnowski.
Society for Neuroscience Abstracts, 1997.

Event-Related Potential (ERP) averages of electrical responses to sensory stimuli recorded at the human scalp capture voltage fluctuations both time locked and phase locked to occurrence of the stimuli. It is widely suspected, though poorly documented, that in single stimulus epochs the response activity may vary widely in both time course and scalp distribution. The major difficulty in comparing single trials is that the spontaneous EEG activity may obscure response-evoked activity, since spontaneous EEG is typically much larger than the evoked response. Independent Component Analysis (ICA) constructs spatial filters that can separate ERPs into spatially-fixed, temporally-sparse components that are temporally independent of one another. By adjusting the amount of ERP and single-trial EEG data used to train the algorithm, the resulting filters can separate larger EEG activity from ERP component activity, allowing a more accurate analysis of changes in the time course and/or the spatial distribution of ERP activity in single trials. Analysis of data from an auditory ERP experiment supports the observation that the relative amplitudes, latencies and scalp distributions of individual ERP components vary independently across single trials from the same subject and session. For example, a component composing N100 may be measurable in some but not all trials, independent of the presence of a component accounting for P300. This suggests that EEG and ERP activity may interact in ways that deserve further study.

Research supported by the Howard Hughes Medical Institute and the Office of Naval Research.

S. Makeig, L. Anllo-Vento, T-P. Jung, A.J. Bell, T. J. Sejnowski and S. A. Hillyard.
Society for Neuroscience Abstracts, 1996.

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.

M. Bartlett, S. Makeig, A.J. Bell, T-P. Jung and T. Sejnowski
Society for Neuroscience Abstracts, 1995.

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 a synthetic 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. Correlations between changes in spectral amplitudes and performance in the ICA channels were more variable than in the raw EEG channels. 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.