Identification of finger flexions from continuous EEG as a brain computer interface

TitleIdentification of finger flexions from continuous EEG as a brain computer interface
Publication TypeConference Paper
Year of Publication1998
AuthorsLisogurski, D., and G. E. Birch
Conference NameEngineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Pagination2004 -2007 vol.4
Date Publishedoct.
Keywordsasynchronous signal detector, Brain computer interface, continuous EEG, continuous sampling, electroencephalography, finger flexions identification, index finger flexions, learning (artificial intelligence), learning VQ, medical expert systems, medical signal detection, medical signal processing, multiple commands recognition, self-organising feature maps, self-organising map, signal classification, single control signal recognition, spatiotemporal features, stand alone interface, surface electrodes, vector quantisation

Much of the research in the development of a Brain Computer Interface (BCI) has focused on differentiating between several possible commands rather than the identification of control signals from continuous EEG. This generally results in the detection of many unintended commands while the operator is trying to rest. This work implements an Asynchronous Signal Detector (ASD) capable of identifying index finger flexions from a continuous sampling of surface electrodes. Spatiotemporal features are classified using Learning Vector Quantization (LVQ). The ASD can function as a stand alone BCI capable of recognizing a single control signal or operate in conjunction with an existing BCI method to recognize multiple commands


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