Emotion-Based Mental State Classification Using EEG for Brain-Computer Interface Applications

Brain-computer interface (BCI) is a growing area of research in human-computer interaction (HCI), where its potential ranges from medicine to entertainment. It intends to manage various assistive technologies through the utilization of brain signals. This technology acquires and interprets brain signals before sending them to a connected device, which generates controls based on the obtained signals. Emotion-based mental state categorization employing electroencephalogram (EEG) signals is an emerging method of BCI application. However, EEG signals comprise artifacts and redundant or noisy information from the subject, equipment, and external environment. Also, the EEG signals have a low spatial resolution (physical location of the activity within the brain) but a high temporal resolution (millisecond level). Therefore, artifact removal, feature extraction, and classification of EEG signals are challenging. This work proposed a novel approach called Extended Independent Component Analysis (E-ICA) for artifact removal from EEG signals. A Multi-class Common Spatial Pattern (M-CSP) is proposed for feature extraction. A Bidirectional long short-term memory (BiLSTM) network is proposed to improve the classification of EEG signals and fine-tune its parameters. This study leverages the Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to validate the model's performance. This dataset includes EEG recordings annotated with emotional attributes such as valence, arousal, dominance, and liking. After conducting several experiments, the proposed approach achieves a high classification accuracy of 94.61% and outperforms state-of-the-art works. The proposed approach can be successfully integrated into BCI systems for real-time emotion identification in healthcare and user engagement detection in gaming environments.