The multimodal parameter enhancement of electroencephalogram signal for music application

Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction, and music class. The weighted stress condition was obtained from stress classes. The EEG signal produces signal features extracted from the frequency domain and time-frequency domain via techniques such as power spectrum density (PSD), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). Power value is evaluated in PSD. The energy distribution is derived from STFT and CWT techniques. Two types of music were used in this experiment. The multimodal fusion is tested using a six-performance measurement method. The purposed multimodal parameter shows the highest accuracy is 97.68%. The sensitivity of this study presents over 95% and the high value for specificity is 89.5%. The area under the curve (AUC) value is 1 and the F1 score is 0.986. The informedness values range from 0.793 to 0.812 found in this paper.