An EEG-fNIRS neurovascular coupling analysis method to investigate cognitive-motor interference

The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling.

Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks.

Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators.

This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.