EEG changes accompanying learned regulation of 12-Hz EEG activity

We analyzed 15 sessions of 64-channel electroencephalographic (EEG) data recorded from a highly trained subject during sessions in which he attempted to regulate power at 12 Hz over his left- and right-central scalp to control the altitude of a cursor moving toward target boxes placed at the top-, middle-, or bottom-right of a computer screen. We used infomax independent component analysis (ICA) to decompose 64-channel EEG data from trials in which the subject successfully up- or down-regulated the measured EEG signals. Applying time-frequency analysis to the time courses of activity of several of the resulting 64 independent EEG components revealed that successful regulation of the measured activity was accompanied by extensive, asymmetrical changes in power and coherence, at both nearby and distant frequencies, in several parts of cortex. A more complete understanding of these phenomena could help to explain the nature and locus of learned regulation of EEG rhythms and might also suggest ways to further optimize the performance of brain-computer interfaces.     

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.     

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, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; 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     

Analyzing high-density ECG signals using ICA

The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.      

Imaging brain dynamics using independent component analysis

The analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain     

Predicting Reaching Targets from Human EEG

Extracting reach information from brain signals is of great interest to the fields of brain-computer interfaces (BCIs) and human motor control. To date, most work in this area has focused on invasive intracranial recordings; however, successful decoding of reach targets from noninvasive electroencephalogram (EEG) signals would be of great interest. In this article, we show that EEG signals contain sufficient information to decode target location during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). We discuss the application of independent component analysis and dipole fitting for removing movement artifacts. With this technique we get similar classification accuracy for classifying EEG signals during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). To the best of our knowledge, this is the first demonstration of decoding (planned) reach targets from EEG. These results lay the foundation for future EEG-based BCIs based on decoding of planned reaches.     

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.     

INDEPENDENT COMPONENT ANALYSIS OF EEG RECORDED DURING TWO-PERSON GAME PLAYING

We report a study on two-person game playing involving simultaneous EEG recording from both subjects. Independent component analysis is used to identify activities of individual cortical EEG sources. Activity of a midline fronto-central component is identified in four of five subjects. This component accounts for the P300 waveform whose amplitude varies, depending on the success in the gaming situation.     

Crowd labeling latent Dirichlet allocation

Large, unlabeled datasets are abundant nowadays, but getting labels for those datasets can be expensive and time-consuming. Crowd labeling is a crowdsourcing approach for gathering such labels from workers whose suggestions are not always accurate. While a variety of algorithms exist for this purpose, we present crowd labeling latent Dirichlet allocation (CL-LDA), a generalization of latent Dirichlet allocation that can solve a more general set of crowd labeling problems. We show that it performs as well as other methods and at times better on a variety of simulated and actual datasets while treating each label as compositional rather than indicating a discrete class. In addition, prior knowledge of workers’ abilities can be incorporated into the model through a structured Bayesian framework. We then apply CL-LDA to the EEG independent component labeling dataset, using its generalizations to further explore the utility of the algorithm. We discuss prospects for creating classifiers from the generated labels.     

Awareness during drowsiness: Dynamics and electrophysiological correlates.

Examined performance patterns and concurrent EEG spectra in 4 Ss (mean age of 30.5 yrs) performing a continuous visuomotor compensatory tracking task in 15–20 min bouts during a 42-hr 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. Results indicate that autocorrelations of time series representing the distance of the target disk from the ring center showed that during periods of poor performance marked near-18-sec 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 behavioral 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 behavioral changes during drowsiness often exhibit stereotyped 18-sec cycles. (PsycINFO Database Record (c) 2010 APA, all rights reserved)