A cortical activity localization approach for decoding finger movements from human electrocorticogram signal

Seyede Mahya Safavi, Alireza S. Behbahani, Ahmed M. Eltawil, Zoran Nenadic, An H. Do

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A novel approach for decoding the finger flexion and extension from the human electrocorticogram is proposed. First, for different finger movements, we use projected MUltiple SIgnal Classification (projected MUSIC) as a source localization technique to estimate the active areas in the primary motor cortex. Next, in order to distinguish between the flexion and extension, the results of the single-trial-based source localizations are fed as the input features to a classifier for decoding. The performance of different techniques such as Support Vector Machine (SVM), Perceptron, and the k-Nearest-Neighbor (kNN) are investigated and the resulting classification accuracies are 71.59, 79.1, and 86.33 respectively.
Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Societyhelp@computer.org
ISBN (Print)9781467385763
DOIs
StatePublished - Feb 26 2016
Externally publishedYes

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