Comparative analysis of signal processing in brain computer interface

Ruiting Yang,Douglas A. Gray,Brian Ng,Mingyi He

Xi'an（2009）

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摘要

Brain computer interface (BCI) systems utilise Electroencephalography (EEG) to translate specific human thinking activities into control commands. An essential part of any BCI is a pattern recognition system. In this paper, a number of different features and classifiers are compared in terms of classification accuracy and computation time. Two typical features are studied: autoregressive (AR) and spectrum components along with three different classifiers; the K-nearest neighbor, linear discriminant analysis (LDA) and Bayesian statistical classifiers. The results showed that all classifiers achieved very high accuracies and short computation times.

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关键词

pattern recognition system,autoregressive components,feature,signal processing,pattern recognition,electroencephalography (eeg),statistical analysis,spectrum components,bayesian statistical classifiers,electroencephalography,brain-computer interfaces,linear discriminant analysis,autoregressive processes,k-nearest neighbor,classifier,brain computer interface,accuracy,spectrum,prototypes,frequency,classification algorithms,k nearest neighbor,feature extraction,brain computer interfaces,rhythm,comparative analysis,signal analysis,bayesian statistics

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