Comparison of Alpha/Beta and high-gamma band for motor-imagery based BCI control: A qualitative study

摘要

Brain waves contain manifold information about ongoing cognitive processes and related body function. This information, such as power changes in certain frequency bands, can be extracted and interpreted by brain-computer interfaces (BCI). For this publication, we re-evaluated data from two subjects with implanted subdural electrodes who participated in a two-class BCI motor imagery experiment with online feedback. In particular, we compared classification accuracy based on bandpower features extracted from a low-frequency band (LFB, Alpha/Beta, 8-32 Hz) and a high-frequency band (HFB, High-Gamma, 110-140 Hz). The signal processing chain involved bandpower computation, spatial filtering via Common Spatial Patterns (CSP), computing the log-normalized variance, and finally classifying via Linear Discriminant Analysis (LDA). For comparison, we also re-evaluated a comparable motor execution experiment with the same participants. Results of the motor imagery experiments revealed that features derived from the LFB provide consistently higher classification accuracies than features derived from the HFB. In contrast to that, HFB-based features outperform LFB-based features for conventional motor execution experiments.