Wearable EEG-IMU based Framework for Investigating Neural Correlates of Motor-Cognitive Interaction in Multiple...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Science

Background:

• Investigating cognitive-motor interactions in multiple sclerosis is crucial for understanding disease progression.
• Technical limitations have previously hindered accurate assessment of these interactions during dynamic tasks.
• Dual-task (DT) assessments are vital for evaluating functional abilities in people with multiple sclerosis (PwMS).

Purpose of the Study:

• To introduce an innovative wearable framework for dual-task (DT) assessment in PwMS.
• To integrate electroencephalography (EEG) and inertial measurement unit (IMU) sensors for simultaneous brain and motion recording.
• To analyze mental workload and cortical activity during cognitive-motor tasks in PwMS.

Main Methods:

• 11 PwMS performed the Timed Up and Go (TUG) test under single-task (ST) and dual-task (DT) conditions.
• A custom bracelet with wearable EEG and IMU sensors recorded brain activity and motion data.
• Hybrid CNN-LSTM model segmented trials, and spectral analysis of EEG (θ and α bands) quantified mental workload.

Main Results:

• The automated segmentation method showed high accuracy (R²=0.993) compared to manual labeling.
• EEG analysis revealed a significant increase in mental workload during the DT condition (p=0.02).
• Cortical activity patterns during DT performance in PwMS are being further investigated with new participants.

Conclusions:

• The proposed framework enables objective assessment of cognitive-motor interactions in PwMS during walking.
• Findings advance the understanding of neural correlates of dual-task performance and fall risk in PwMS.
• This technology can inform personalized rehabilitation strategies for individuals with multiple sclerosis.