A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data

• Published in: Pattern recognition letters 2010-08, Vol.31 (11), p.1207-1215
• Main Authors: Kayikcioglu, Temel, Aydemir, Onder
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Brain computer interface (BCI); Classification; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Connectionism. Neural networks; Electroencephalogram (EEG); Exact sciences and technology; Feature extraction; Information, signal and communications theory; k-Nearest neighbor; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Polynomial fitting; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2010.04.009

Speed and accuracy in classification of electroencephalographic (EEG) signals are key issues in brain computer interface (BCI) technology. In this paper, we propose a fast and accurate classification method for cursor movement imagery EEG data. A two-dimensional feature vector is obtained from coefficients of the second order polynomial applied to signals of only one channel. Then, the features are classified by using the k-nearest neighbor ( k-NN) algorithm. We obtained significant improvement for the speed and accuracy of the classification for data set Ia, which is a typical representative of one kind of BCI competition 2003 data. Compared with the Multiple Layer Perceptron (MLP) and the Support Vector Machine (SVM) algorithms, the k-NN algorithm not only provides better classification accuracy but also needs less training and testing times.