EEG based multifunctional connectivity fusion across frequency bands and parameters promote motor function assessment in stroke: a pilot study

BACKGROUND: Individual phase indexes in functional connectivity as biomarkers for assessing motor function in stroke patients can be affected by noise and volume conduction, resulting in unreliable and poorly correlated assessments. METHODS: To explore whether multifunctional connectivity index fusion can effectively assess motor function in patients with chronic stroke. This study included 12 participants with chronic stroke and 12 healthy controls. EEG data from 14 channels near the primary motor cortex area (M1) was recorded. Fugl-Meyer scores (FMAU/FMAL) were assessed at admission. Then, correlations between PSI, PLI, WPLI differences, and FMAS between groups were analyzed. Cross-index and cross-band fusion based on validated biomarkers were performed to assess patients’ motor impairment. RESULTS: PSI, PLI, and WPLI of the patients with M1 were lower than those in the controls in the low-alpha. PSI and PLI were significantly correlated with FMAS and FMAU in low-alpha, and WPLI showed a strong correlation only with FMAU in low-alpha and high-beta bands. In the fusion assessment, PSI at low alpha and WPLI at low alpha showed a 13.7% improvement in correlation over single metrics (r = 0.8308, p = 0.0008). Low-alpha PSI and high-beta fusion presented a similar improvement (r = 0.8273, p = 0.0018), with a 14.3% improvement. CONCLUSIONS: The study demonstrates that multifunctional connectivity index fusion is strongly associated with motor function. Therefore, the complementary strengths of phase indexes may provide new insights for assessing motor function in stroke patients.