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Learning discrimination trajectories in EEG sensor space: application to inferring task difficulty

An Luo and Paul Sajda

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We describe a spatio-temporal linear discriminator for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required, i.e., the algorithm requires no knowledge about the timing and spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory-evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment.


PACS

87.80.-y Biophysical techniques (research methods)

87.19.R- Mechanical and electrical properties of tissues and organs

Subjects

Instrumentation and measurement

Medical physics

Biological physics

Dates

Issue 1 (March 2006)

Received 27 June 2005, accepted for publication 3 November 2005

Published 19 December 2005



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