Classification of EEG Signal for Body Earthing Application

Abstract

Stress is the way our body reacts to the threat and any kind of demand. Stress happens when your nervous system releases the stress hormones including adrenaline and cortisol that lead to an emergency response of the body. Body earthing technique is used to resolve this problem. Body earthing is a method that is used to neutralize positive and negative charge in the human body by connecting to the earth. EEG signals can be used to verify the positive effect of body earthing. This project focuses on the classification of EEG signals for body earthing application. First, EEG signals from human brainwaves were recorded by using Emotive EPOC Headset, before and after body earthing for the 30 subjects. The alpha band and the Beta band were filtered by using Band-pass filter ‘Butterworth’. After filtering, the threshold of signal amplitude was set in the range of -100 μV to 100 μV in order to remove the noise or artifact. For feature extraction, Short-time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used. Lastly, the Artificial Neural Network (ANN) model is employed to classify EEG signal taken from samples, before and after the body earthing. A number of neurons chosen for this project are 55 with the mean square error 0.0023738. The result showed that Alpha band signals before body earthing are low compared to after body earthing. Whereas, for the Beta band signals, the result before body earthing is high compared to after body earthing. The increased signals of the Alpha band show that subjects are in relax state, while the decreased of Beta band signals shows the sample in stress state. These results imply for both features of STFT and CWT. Based on the confusion matrix, the result for the ANN classification yields 86.7% accuracy.