EEG differentiates left and right imagined Lower Limb movement

Background: Identifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature.

Research question: Can left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)?

Methods: An experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively.

Results: Findings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %.

Significance: Our study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility.