Neurophysiology-inspired neural networks for affective brain-computer interfaces - Ding, Yi (Nanyang Technological University, 2023)
Brain-computer interface (BCI) facilitates the computer's perception of human emotional mental states through electroencephalogram (EEG), also known as affective brain-computer interface (aBCI). An aBCI can be applied in emotion-focused therapy (EFT) for psychological disabilities, such as generalized anxiety disorder (GAD) and autistic spectrum disorders (ASD). To ensure reliable real-world implementation, an effective aBCI system requires accurate emotion detection and high generalization capability of decoding algorithms. However, emotion recognition using EEG remains challenging due to factors such as low signal-to-noise ratio (SNR), non-stationary nature of EEG signals, and the complexity of cognitive processes underlying emotions. In recent times, deep learning methods have demonstrated promising results across various domains, including computer vision, natural language processing, audio signal processing, and several sub-areas of BCI. Successful implementation of deep learning methods relies on factors such as abundant data, effective training methods, and access to ample computational resources. Equally important is the design of a well-structured neural network architecture capable of capturing the intrinsic characteristics of the data. Through a comprehensive evaluation of state-of-the-art (SOTA) methods in the BCI domain under various assessment settings, this thesis highlights the persistent challenges faced in EEG-based emotion recognition tasks, particularly in generalized evaluation scenarios. This thesis focuses on incorporating insights from neurophysiology studies into neural network design, aiming to enhance the decoding performance of emotions using EEG signals.