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Classification of Delta Band Motor Imagery EEG Signals in SCI Patients using the Regularized Common Temporal Pattern Method

Authors :

Mahdi Babaei¹ Sorena Shadzinavaz² Sepideh Hajipour Sardouie³

1- دانشگاه صنعتی شریف 2- دانشگاه صنعتی شریف 3- دانشگاه صنعتی شریف

Keywords :

Brain Computer Interface،Spinal Cord Injury،Motor Imagery،Feature Extraction،Regularized Common Temporal Pattern

Abstract :

Accurate classification of motor imagery tasks is essential for enhancing brain–computer interfaces (BCIs) in spinal cord injury (SCI) rehabilitation. The use of raw time samples often overlooks temporal dependencies in electroencephalography (EEG), limiting classification accuracy. We propose the Regularized Common Temporal Pattern (RCTempP), a novel time-domain feature extraction method that emphasizes discriminative temporal samples. RCTempP was evaluated on EEG recordings from SCI patients imagining five distinct hand movements within the 0.3–3 Hz band. Compared to raw time samples, Common Temporal Pattern (CTP), and Extended CTP (ECTP) approaches, RCTempP yielded statistically significant improvements in eight out of ten class pairs, for which the average accuracy across subjects ranged from 70.4% to 75.1%. Performance was assessed using a fivefold cross-validation protocol to ensure robust and generalizable results. Importantly, RCTempP’s significant temporal filters emerged post‐cue onset and corresponded to movement‐related cortical potential peaks. These findings highlight RCTempP’s promise for advancing motor imagery BCIs in SCI rehabilitation.