Brain Computer Interface (BCI) is a system that provides a non-muscular communication between men and machines. This paper aims at classification of motor (hand movement) imagery to facilitate control for physically challenged persons using EEG signals. The work involves a scheme based on tensors. Advantages of this scheme over conventional schemes like Common Spatial Patterns (CSP), Linear Discriminant Analysis (LDA) are that 1. The number of parameters required to model the data is reduced, 2. This scheme works well without pre-processing (filtering, artifact removal etc.) of EEG signals, 3. Undersampling problem (number of training samples is less than dimension of data) is reduced. The work employs wavelet transform for representing EEG signals as tensors, General Tensor Discriminant Analysis (GTDA) for dimensionality reduction and Support Vector Machines for classification. Applications to datasets show the efficiency of this scheme compared to CSP and LDA. The work is expected to open new and higher levels of control for BCI since preprocessing is not needed.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@6473c15a ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@6b9b2f3b Through org.apache.xalan.xsltc.dom.DOMAdapter@193a01ae; Conference Code:84089
C. Vab Nagendhiran, Kumar, Mab Ashok, Kharthigeyan, S. Sab, Naveen, Lab, and Prasanna, Sab Sai, “Tensor scheme using GTDA for EEG mental task classification”, in 10th WSEAS International Conference on Wavelet Analysis and Multirate Systems, WAMUS '10, 9th WSEAS International Conference on Non-Linear Analysis, Non-Linear Systems and Chaos, NOLASC '10, Sousse, 2010, pp. 83-88.