Brain-computer interface for mental attention

A brain-computer interface (BCI) records, processes, and translates brain activity into commands for an interactive application. This thesis mainly addresses the attention-related challenges that electroencephalography (EEG)-based BCI systems face, including assessment of subject’s attention status using EEG-based BCI, continuous attention detection from EEG, and improving the BCI performance under attention diversion. Firstly, a correlation analysis between EEG and attentional behaviour is performed to find the EEG attention-representative features. These features are then used to assess the attention status that is measured by a neurophysiological assessment test. Attention status shows how well is the functioning of the attention domain. The results show the effectiveness of EEG in the assessment of attention status and thus verify the feasibility of attention detection using EEG. Subsequently, deep learning (DL) method is used to learn hidden information in the EEG for attention detection. We propose an end-to-end DL-based framework with subject-to-subject transfer learning strategies. The results show that the proposed methods significantly outperform state-of-the-art methods. Moreover, visualization of the deep neural network’s perceived input of attention and non-attention demonstrates that the proposed framework truly learns meaningful information from the EEG data. Last but not least, an experiment that includes focused and diverted attention conditions is designed and implemented to investigate the effect of attention diversion on the performance of BCI in the detection of movement intention. A significant drop in the BCI performance under the diverted attention condition was observed. To improve the performance, we propose a novel approach based on generative adversarial networks (GANs) to augment EEG. The results show that the proposed method significantly improves the BCI performance. The research presented in this thesis firstly shows the effectiveness of EEG-based BCI in the assessment of attention status and thus the feasibility of attention detection using EEG. Subsequently, the thesis proposes a novel method for continuous attention detection from EEG that shows superior results over baseline methods in subject-to-subject classification. The interpretability of the results and the generalizability of the method are other advantages of the proposed method. Lastly, the thesis proposes a data augmentation method that improves the BCI performance under attention diversion which is a challenging condition in real-life applications of BCI. The present study can contribute to the improvement of cognitive BCI systems, especially those developed for attention training/treatment, and can be further extended to other BCI applications.Doctor of Philosoph