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Improved recognition of error related potentials through the use of brain connectivity features

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5 Author(s)
Huaijian Zhang ; Center for Neuroprosthetics, Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland ; Chavarriaga, R. ; Goel, M.K. ; Gheorghe, L.
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Brain error processing plays a key role in goal-directed behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in brain-computer interface (BCI) system. We describe the first application of brain connectivity features for recognizing error-related signals in non-invasive BCI. EEG signal were recorded from 16 human subjects when they monitored stimuli moving in either correct or erroneous direction. Classification performance using waveform features, brain connectivity features and their combination were compared. The result of combined features yielded highest classification accuracy, 0:85. In addition, we also show that brain connectivity at theta band around 200ms after stimuli carry highly discriminant information between error and correct trials. This paper provides evidence that the use of connectivity features improve the performance of an EEG based BCI.

Published in:

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Date of Conference:

Aug. 28 2012-Sept. 1 2012