Integrated EEG-fNIRS for Characterizing Cortical Responses and Neurovascular Coupling in Automated and Discrete Gait...

Area of Science:

• Neuroscience
• Motor Control
• Biomedical Engineering

Background:

• Cortical responses during walking are complex, involving both automated and cognitive processes.
• Previous research often conflates basic gait control with cognitive demands, limiting understanding of distinct neural mechanisms.
• High spatiotemporal resolution techniques are needed to differentiate neural activity during varied gait tasks.

Purpose of the Study:

• To establish an integrated electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) framework for characterizing cortical responses during gait.
• To differentiate neural and neurovascular coupling patterns between automated and discrete gait tasks.
• To assess the efficacy of Task-Related Component Analysis (TRCA) in analyzing multimodal EEG-fNIRS data for gait tasks.

Main Methods:

• Simultaneous EEG-fNIRS recordings were obtained from 18 healthy participants performing continuous walking (CW), isolated gait phase (IGPT), and single-limb stance (SS) tasks.
• Task-Related Component Analysis (TRCA) was employed to extract task-specific features from EEG and fNIRS signals.
• Neurovascular coupling was assessed using cross-correlation analysis, and task classification was performed using XGBoost.

Main Results:

• Beta-band suppression was significantly stronger during IGPT compared to CW (p = 0.040), and SS tasks showed higher fNIRS activation than CW (p = 0.026).
• TRCA significantly improved the discriminability of EEG-fNIRS features across tasks (p < 0.05) and revealed task-specific alpha-band neurovascular coupling.
• Multimodal TRCA-based fusion achieved 74.51% classification accuracy, outperforming unimodal EEG-Avg (49.02%, p = 0.042) and fNIRS-Avg (47.06%, p = 0.038).

Conclusions:

• The developed EEG-fNIRS framework effectively distinguishes between automated and discrete gait tasks by revealing unique cortical responses and neurovascular coupling.
• TRCA enhances the analysis of multimodal neuroimaging data, improving the characterization of gait-related neural activity.
• This study provides a foundation for understanding gait control mechanisms and developing targeted neurorehabilitation strategies.