Classification of the lower limb motor imagery and execution using high vs. low channel EEG devices

Brain-computer interfaces (BCIs) translate brain signals into commands for external devices, with motor imagery (MI) BCIs decoding imagined movements to aid neurorehabilitation. Although high-channel EEG offers rich data, such systems are bulky and impractical for everyday use. This study assesses whether a low-channel, consumer-grade headset (Muse) can match a clinical-grade system (OpenBCI) in classifying lower limb MI and motor execution (ME). Six healthy volunteers performed left and right knees and ankles MI and ME tasks while EEG was recorded concurrently from both devices. Signals were band-pass filtered (8-30 Hz), segmented into overlapping one second windows, and features were extracted across time, frequency, and time-frequency domains. Feature dimensionality was reduced via mutual information-based minimum redundancy maximum relevance and principal component analysis. Five classifiers (support vector machine, linear discriminant analysis, k nearest neighbors, random forest, and AdaBoost) were applied to nine binary discrimination scenarios and evaluated with 10-fold cross-validation via 100 Monte Carlo iterations. Frequency domain features, particularly those derived from Welchs power spectral density, were most frequently selected. Mutual information analysis indicated that C3 and C4 electrodes were most informative for OpenBCI, while in Muse, the channels contributed more evenly, except in laterality classification scenarios, where TP9 played a key role. OpenBCI outperformed Muse in classifier-based accuracy with superiority ranging from 0.4% to 4.8%, while task-based differences were more variable, ranging from -0.3% to 8.7%. Despite its lower spatial resolution, the Muse system achieved competitive performance, especially in motor vs. rest tasks, and shows promise as an affordable, user-friendly alternative for home-based neurorehabilitation BCIs.