EEG Signal-Based Human Emotion Recognition Using Power Spectrum Density and Discrete Wavelet Transform

Emotion recognition has been a problem in the field of brain–computer interface. Numerous ways are available for recognizing human emotions and one such technique is through Electroencephalogram (EEG) signals. EEG signals are recordings of the subject’s electrical activity in the brain. Feature extraction approaches such as Power Spectrum Density (PSD) and Discrete Wavelet Transform (DWT) are fed as features to various machine learning (ML) and deep learning (DL) models. This work aims to develop models that predict emotions from EEG data. In addition, the results of the above-mentioned feature extraction approaches are compared in this work. The proposed feature extraction methods and models are applied on the DEAP dataset.