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|Title:||Comparative analysis of signal processing in brain computer interface|
|Citation:||Proceedings of the 4th IEEE Conference on ICIEA 2009: pp.580-585|
|Conference Name:||IEEE Conference on Industrial Electronics and Applications (4th : 2009 : Xi'an, China)|
|Ruiting Yang, D.A Gray, B.W Ng and Mingyi He|
|Abstract:||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.|
|Keywords:||Electroencephalography (EEG); brain computer; interface; classifier; feature|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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