Analysis of a BMI System for the Assessment of Cognitive Motor Engagement for the Neurorehabilitation of Patients with Lower-Limb Impairment.

Brain-Machine Interfaces (BMIs) hold significant promise for the neurorehabilitation of patients with lower-limb impairments. However, their widespread clinical adoption is hindered by high costs and system complexity. This study presents an open-loop, low-cost EEG-based BMI designed to assess the cognitive implication of users during assisted cycling therapy. The system computes two cognitive indices: a low-frequency index, associated with motor-related engagement, and a high-frequency index, related to attention during motor tasks. These indices are obtained using Filter Bank Common Spatial Patterns (FBCSP) and Linear Discriminant Analysis (LDA), enabling continuous monitoring of cognitive involvement. Leave-One-Out Cross-Validation (LOOCV) results showed a clear increase in both indices during motor engagement compared to relaxed states in healthy subjects (low-frequency: [Formula: see text] to [Formula: see text]; high-frequency: [Formula: see text] to [Formula: see text]), whereas patients exhibited smaller increments (low-frequency: [Formula: see text] to [Formula: see text]; high-frequency: [Formula: see text] to [Formula: see text]). During validation trials, index differences between relax periods and motor engagement periods reveal a mean separation of [Formula: see text] (low-frequency) and [Formula: see text] (high-frequency) in control subjects, compared to [Formula: see text] and [Formula: see text] in patients. Results suggest that the metrics obtained by the present BMI can be a useful asset to evaluate patients' performance, engagement and fatigue during neurorehabilitation.