Classifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor Imagery | Science Publications

Journal of Computer Science

V. B. Deepa¹  S. Chitra¹  P. Thangaraj¹ 

关键词: Data mining;    Brain-Computer Interface (BCI);    Fast Hartley transform (FHT);    Electroencephalogram (EEG);    Motor Imagery (MI);    Common Spatial Pattern (CSP);    Event-Related Desynchronization/Synchronization (ERD/ERS);    Discrete Wavelet Transform (DWT);    Fourier Transform (FT);   

DOI  :  10.3844/jcssp.2011.757.761

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: Brain-Computer Interface (BCI) is a emerging research area whichtranslates the brain signals for any motor related actions into computer understandable signals bycapturing the signal, processing the signal and classifying the motor imagery. This area of work findsvarious applications in neuroprosthetics. Mental activity leads to changes of electrophysiologicalsignals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). Approach: The BCIsystem detects such changes and transforms it into a control signal which can, for example, be used asto control a electric wheel. In this study the BCI paradigm is tested by our proposed Gaussiansmoothened Fast Hartley Transform (GS-FHT) which is used to compute the energies of differentmotor imageries the subject thinks after selecting the required frequencies using band pass filter.Results: We apply this procedure to BCI Competition dataset IVA, a publicly available EEGrepository. Conclusion: The evaluations of preprocessed signals showed that the extracted featureswere interpretable and can lead to high classification accuracy by various mining algorithms.

【 授权许可】

Unknown