Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects
This work addresses the need for more accurate and less time-consuming BCI recalibration in rehabilitation for stroke patients and healthy subjects, though it is incremental as it builds on existing EEG feature methods.
The paper tackled the problem of detecting gait intention from EEG signals for brain-computer interfaces in rehabilitation, showing that using phase features of movement-related cortical potentials improves accuracy, with results like 66.5% correct detection in healthy subjects and 63.3% in stroke patients for session-specific calibration.
One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention. We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions. Phase features were compared to more conventional amplitude and power features. The neurophysiology analysis showed that phase features have higher signal-to-noise ratio than the other features. Also, BCI detectors of gait intention based on phase, amplitude, and their combination were evaluated under three conditions: session specific calibration, intersession transfer, and intersubject transfer. Results show that the phase based detector is the most accurate for session specific calibration (movement intention was correctly detected in 66.5% of trials in healthy subjects, and in 63.3% in stroke patients). However, in intersession and intersubject transfer, the detector that combines amplitude and phase features is the most accurate one and the only that retains its accuracy (62.5% in healthy subjects and 59% in stroke patients) w.r.t. session specific calibration. Thus, MRCP phase features improve the detection of gait intention and could be used in practice to remove time-consuming BCI recalibration.