| dc.description.abstract | Emotions is a very important parameter in Brain-Computer Interface, as it helps the computer
to accurately identify the user’s intentions with their commands. However, it is a difficult task to
identify human emotions as there are many variations to the outward physiological expressions
(facial expressions, tone of voice, etc). The proposed method aims to apply machine learning in
processing electroencephalogram (EEG) signals, which is tied to brain activity, to identify human
emotions by predicting Valence and Arousal values using 4 regressors: k-Nearest Neighbours
(KNN), Support Vector Machine for Regression (SVR), Random Forest (RF), and Linear
Regression (LR). The EEG signal used is the DEAP dataset with 4 features: power spectral density
(PSD), wavelet energy (WP), wavelet entropy (WE), Hjorth’s mobility (H2) and complexity (H3)
extracted from 4 frequency bands: theta (4 - 8 Hz), alpha (8 - 12 Hz), beta (12 - 30 Hz), and gamma
(30 – 64 Hz) using Welch’s periodogram estimation, Discrete Wavelet Transform and Hjorth
parameters. Cross-validation and feature standardization is then employed to process the features
before being fitted into the machine learning algorithms. The results show that the best predictions
are made by KNN and SVR with beta and gamma-based features. | en_US |
