SAM Technology and EEG Systems Lab
101 Spear St. #203
San Francisco, CA 94105
alan@eeg.com
415-227-4900 X133
FAX 415-546-7122
In one recent study, features of the EEG related to mental effort were identified and extracted and effective automated means for classifying the features according to degree of task difficulty were developed and tested. To do this, an experiment was designed to systematically vary task difficulty (in terms of increasing working memory load) while holding stimulus- and response-related factors constant. Performance and electrophysiological data were collected from eight subjects while they performed tasks that taxed spatial and verbal working memory, functions which are critical for performance of most complex computer-based tasks. When neural-network pattern recognition classification functions were applied to automatically discriminate high and low workload levels, over 95% accuracy was obtained on independent testing data. Further, networks trained on data from one day were found to reliably classify data collected on another day, networks trained on data from one task were found to be general enough to accurately classify data collected in another similarly well-controlled task, and networks trained on data from a group of subjects were found to be general enough to classify data from new subjects who were not part of the training group.
Supported by NSF and AFOSR
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