Real-time motion artifact detection and removal for ambulatory BCI

Although human cognition often occurs while moving, most studies of the dynamics of the human brain examine subjects while static and seated in a highly controlled laboratory. EEG signals have been considered to be too noisy to record brain dynamics during human locomotion. Here, we present a real-time ambulatory brain computer interface which allows us to detect gait phases and remove motion-related artifacts from EEG signals during walking in real-world environments. We first construct stride-based artifact templates employing a gyroscope to measure the angular velocity of the human body. Then, we apply an adaptive Kalman filter to estimate the mapping between the stride-based artifact template and EEG space, subtracting the motion-related noise from the raw EEG signal. This study demonstrates the robustness of our system to remove gait-related movement artifacts during human locomotion. Experiments in real-world environments show the potential practicality of reallife applications of low-cost wearable and wireless BCI systems for users actively working in and interacting with their environments.