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|Title:||Multilayer Perceptrons for the classification of Brain Computer Interface data||Authors:||Balakrishnan, D.
|Issue Date:||2005||Citation:||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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