Real-time Phase Locked Neural Feedback System Development

The neural oscillation in electroencephalogram (EEG) signals is highly related to people’s psychological cognitive ability. In this work, to achieve optimal real-time modulation effect of alpha wave, we first conduct comprehensive delay analysis and technology selection to propose the ideal architecture of neural feedback system.

After measurement and analysis of hardware delay, software delay, we propose an OpenBCI version of phase-locked feedback control system has been implemented for real time alpha wave regulation. As compared with the distributed system architecture on Brainamp, PC and Arduino in the previous work, the new proposed system has integrated all the modules for signal acquisition, phase estimation and applying stimulation on one chip. Hence, the delay for signal transmission can be effectively eliminated, which leads to a better accuracy for phase estimation in phase-locked feedback control. The results show the effective of the proposed system to alpha wave regulation, which has significant modulation advantages in amplitude and frequency compared to Distributed BrainProduct Architectures.

Besides, we developed a PyQtGraph-based visualization tool for real-time monitoring on host computer, streaming data from OpenBCI board by Bluetooth 2.0 protocols.