Brain-Computer Interface in dynamic tasks with deep learning and functional connectivity analysis

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Abstract The PI proposes a high-impact multi-disciplinary research project to develop and validate machine learning al- gorithms for shift-detection in electroencephalogram (EEG) signals with applications to brain-computer interface to make them more reliable. Brain-computer interface is a means of communication for severely disabled peo- ple by decoding brain responses and translating their detection into commands with applications such a virtual keyboard or robotic control systems. Current brain-computer interface systems cannot be efficiently deployed in clinical setting due to their inability to properly take into account the non-stationarity properties of the evoked brain responses in the electroencephalogram signal. This project aims at enhancing the brain decoding perfor- mance when the task changes over time. The PI proposes to investigate the effects of well defined types of data shifts: covariate shift, probability shift, and concept shift to enhance brain decoding performance in changing tasks. The goals of this proposal are: 1) to characterize in event related potential (ERP) components neural signatures corresponding to task changes by using EEG recordings and machine learning techniques for single- trial detection. 2) to research in functional brain connectivity neural signature corresponding to task changes by using EEG recordings and directed model-based and model free techniques of functional brain connectivity. 3) to combine and adapt machine learning techniques to detect when changes occur during a task. This proposal will significantly improve the infrastructure of research and education at California State University Fresno, Hispanic- Serving Institution and an Asian American and Native American Pacific Islander-Serving Institution, introducing biomedical engineering research experiences to underrepresented minority and female students in computer science and psychology students. This would allow them to experience different stages of the scientific method, and acquire fundamental skills related to data science applied to physiological signals with potential impact on society for improving the life of severely disabled people.