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Optimal determination of wavelet for football player EEG using SVM classifier

Ashish Aggarwal, Ravinder Agarwal

This study investigates EEG for evaluation of the cerebrum and cognitive processes for sports’ applications. An infamous issue with EEG signals for the cerebral information is frequently contaminated by artifacts of the non-cerebral cause. The nearness of curios makes the examination of EEG troublesome for sports’ applications. And, to manage these artifacts, various strategies and procedures have been developed by numerous specialists applying conventional filters, artificial intelligence and time-frequency based techniques. Wavelet Transform outperforms for denoising nonstationary EEG signals, but the performance of this technique is highly dependent on wavelet selection. This paper explores an appropriate selection of particular wavelet which presents a modular statistical framework providing a pathway. A comparative analysis of 6 different wavelet families for effective filtering was done. The results show that biorthogonal wavelet family (bior3.1) was more reasonable and a classification accuracy of 91.68% was achieved for denoising the above-stated signals.