Publication Type : Conference Proceedings
Publisher : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India.
Source : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, p.550-553 (2018)
Keywords : Accuracy, ANN, approximation coefficients, Artificial neural networks, BCI, Brain, cepstral analysis, classification, Control applications, Discrete Wavelet Transform, Discrete wavelet transforms, DWT, EEG, EEG based brain control techniques, EEG based control, EEG data, Electroencephalography, entropy, Feature extraction, k-nearest neighbor, kNN, medical signal processing, neural nets, Neurophysiology, Pre-processing, pre-processing techniques, raw EEG signals, Sensitivity, severely disabled people, signal quality, specificity, Statistical parameters, strong aid, Supervised learning, Wavelet analysis, wavelet cepstrum, Wavelet domain, wavelet entropy, Wavelet features, wireless headset .
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : EEG based brain control techniques serves as a strong aid for severely disabled people, as it gives the direct measure of the cortical activity of brain. The work aims at analyzing and classifying the eye blinks obtained from EEG signals for control applications. A wireless headset consisting of 14 terminals was used for acquiring EEG data from 10 healthy subjects. In order to improve the signal quality in raw EEG signals, pre-processing techniques for removing noise and baseline variations were applied. Further Discrete wavelet transform (DWT) was used for extracting required features. Features in wavelet domain: wavelet entropy, wavelet cepstrum and statistical parameters from the approximation coefficients were used for supervised learning and classification. The analysis was carried out for three levels of decomposition using Daubechies 6 wavelet (db6). The system performance was evaluated using the K-Nearest Neighbor and Artificial Neural Networks using the measures: accuracy, sensitivity and specificity
Cite this Research Publication : Poorna S. S., Raghav, R., Nandan, A., and Nair, G. J., “EEG Based Control - A Study Using Wavelet Features”, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Bangalore, India, pp. 550-553, 2018.
