Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71043
Title: Multilayer Perceptrons for the classification of Brain Computer Interface data
Authors: Balakrishnan, D.
Puthusserypady, S. 
Issue Date: 2005
Source: Balakrishnan, D.,Puthusserypady, S. (2005). Multilayer Perceptrons for the classification of Brain Computer Interface data. Bioengineering, Proceedings of the Northeast Conference : 118-119. ScholarBank@NUS Repository.
Abstract: Fast and simple classification methods for Brain Computer Interfacing (BCI) signals are indispensable for the design of successful BCI applications. This paper presents a computationally simple algorithm to classify BCI data into left and right finger movements of the subjects. A two-class output Multilayer Perceptron (MLP) performs the classification. Our approach is attractive for providing an optimal combination of 1) computational efficiency 2) classification accuracy (Training: 100% and testing: 64%) and 3) minimal feature extraction (two channels out of a 28-channel EEG trial). The channels selected to be extracted (C3 and C4) not only greatly reduce dimensionality, but also refer to the central parts of the brain that decide left- right cognition, greatly enhancing the classification task. The results obtained are promising, and hold much potential for further investigation. © 2005 IEEE.
Source Title: Bioengineering, Proceedings of the Northeast Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/71043
ISSN: 1071121X
Appears in Collections:Staff Publications

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