a b s t r a c tPrior studies of multichannel ECoG from animals showed that beta and gamma oscillations carried perceptual information in both local and global spatial patterns of amplitude modulation, when the subjects were trained to discriminate conditioned stimuli (CS). Here the hypothesis was tested that similar patterns could be found in the scalp EEG human subjects trained to discriminate simultaneous visual-auditory CS. Signals were continuously recorded from 64 equispaced scalp electrodes and band-pass filtered. The Hilbert transform gave the analytic phase, which segmented the EEG into temporal frames, and the analytic amplitude, which expressed the pattern in each frame as a feature vector. Methods applied to the ECoG were adapted to the EEG for systematic search of the beta-gamma spectrum, the time period after CS onset, and the scalp surface to locate patterns that could be classified with respect to type of CS. Spatial patterns of EEG amplitude modulation were found from all subjects that could be classified with respect to stimulus combination type significantly above chance levels. The patterns were found in the beta range (15-22 Hz) but not in the gamma range. They occurred in three short bursts following CS onset. They were non-local, occupying the entire array. Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function, so that future studies in brain-computer interfacing can be better focused. Our methods may be most valuable for analyzing data from dense arrays with very high spatial and temporal sampling rates.

Obtaining an ECoG signal requires an invasive procedure in which brain activity is recorded from the cortical surface. In contrast, obtaining EEG recordings requires the non-invasive procedure of recording the brain activity from the scalp surface, which allows EEG recordings to be performed more easily on healthy humans. In this work, a technique previously used to study spatial-temporal patterns of brain activity on animal ECoG was adapted for use on EEG. The main issues are centered on solving the problems introduced by the increment on the interelectrode distance and the procedure to detect stable frames. The results showed that spatial patterns of beta and gamma activity can also be extracted from the EEG signal by using stables frames as time markers for feature extraction. This adapted technique makes it possible to take advantage of the cognitive and phenomenological awareness of a normal healthy subject.

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