Optimal Accuracy Performance in Music-Based Eeg Signal Using Matthew Correlation Coefficient Advanced (Mcca)

Abstract

The connection between music and human are very synonyms because music could reduce stress. The state of stress could be measured using EEG signal, an electroencephalogram (EEG) measurement which contains an arousal and valence index value. In previous studies, it is found that the Matthew Correlation Coefficient (MCC) performance accuracy is of 85±5%. The arousal indicates strong emotion, and valence indicates positive and negative degree of emotion. Arousal and valence values could be used to measure the accuracy performance. This research focuses on the enhance MCC parameter equation based on arousal and valence values to perform the maximum accuracy percentage in the frequency domain and time-frequency domain analysis. Twenty-one features were used to improve the significance of feature extraction results and the investigated arousal and valence value. The substantial feature extraction involved alpha, beta, delta and theta frequency bands in measuring the arousal and valence index formula. Based on the results, the arousal and valance index is accepted to be applied as parameters in the MCC equations. However, in certain cases, the improvement of the MCC parameter is required to achieve a high accuracy percentage and this research proposed Matthew correlation coefficient advanced (MCCA) in order to improve the performance result by using a six sigma method. In conclusion, the MCCA equation is established to enhance the existing MCC parameter to improve the accuracy percentage up to 99.9% for the arousal and valence index.

The multimodal parameter enhancement of electroencephalogram signal for music application

“…In the example, the recognition of high arousal degree in the EEG signal for the intention complex task used 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) are collected by Emotiv headband [6]. In signal processing, a bandpass filter in the EEG signal processing analysis is practically applied to obtain the EEG frequency range from 0.01 Hz until 100 Hz with an approximate amplitude of 100 μV [7]. Then the comprehensive features of EEG signals were extracted into various connectivity functions based on primary frequency bands which are delta, theta, alpha, beta, and gamma [8].…”

Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review

“…40,[42][43][44]46,47,[52][53][54][55][56][57][58][59][60][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81]84,86,[88][89][90][91][92]96], while the remainder (specifically 17, 21%) were conference papers…”