EEG Feature extraction using DaubechiesWavelet and Classification using Neural Network - kumar, Krishna NJ; Balakrishna, R (International Journal of Computer Sciences and Engineering, 2019)
Electroencephalography (EEG) is a straightforward technique which gives thought regarding the potential produced on the outside of the mind which helps in understanding the usefulness of the cerebrum. EEG signals play a vital job in recognizing the human feelings. In feeling appraisal using EEG flags, the time span of EEG motions in given number of channels, enthusiastic upgrades, and recurrence groups, nature of statistical feature extraction techniques and highlights important job. In this paper, new highlights are removed using Discrete Wavelet Transform (DWT) and further the feelings are arranged using EEG signs of 10 subjects is gathered and using 24 anodes from the standard 10-20 Electrode Placement System which is set over the whole scalp. Feature Extraction is performed by using DWT and the Decomposition of EEG signals is separated for 8 levels using "db4" wavelet. The feature extracted signs are then grouped using Artificial Neural Network (ANN) and the neural framework which can be compared at for feeling passionate states classification.