Exploring Kinematics Information Decoding from EEG Slow Cortical Potentials During Movement Imagination and Observation.

Motor Imagery-based brain computer interface (MI-BCI) systems capable of decoding imagined movements and their kinematics are a rapidly advancing area of BCI research. Such BCIs can enhance human-computer interaction and have potential neurorehabilitation and assistive technology applications. This study explores the feasibility of decoding kinematic information, including movement direction and speed of imagined hand movements, from EEG slow cortical potentials (SCPs). EEG data from fourteen healthy subjects, associated with bidirectional center-out right-hand movement imaginations at two different speeds, is analyzed in this study. Peak negativity of movement-related cortical potential derived from fifteen primary motor cortex EEG channels is used to decode the direction and speed of imagined and observed hand movements. A Pearson correlation coefficient-based channel selection is further applied to identify a subject-specific set of channels from the pool of fifteen channels for decoding the kinematic information. Pairwise classification of direction-speed combinations achieved an average accuracy of 63.44 ± 9%. In contrast, slow-versus-fast speed classification achieved a lower accuracy of 53.87 ± 6.4% for motor imagery, which was not significantly different from the empirical chance distribution. The same analysis applied to movement observation resulted in an average direction-speed pair classification accuracy of 57.74 ± 8.6%, while speed classification achieved 50.74 ± 8.1%. These findings demonstrate that SCP features contain reliable information related to movement direction, whereas speed-related information appears weaker and less consistent across subjects. The results highlight the potential of SCP-based decoding for directional control and motivate further investigation of speed-related neural signatures. The findings from direction decoding during movement observation open avenues for future investigations into shared neural representations underlying passive movement observation.