Investigating Brain Connectivity and Information Flow in Mental Workload Using EEG and fNIRS Integration
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915596691570688 |
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| author | Qaremohammadlou, Mohaddese Shamsollahi, Mohammad Bagher |
| author_facet | Qaremohammadlou, Mohaddese Shamsollahi, Mohammad Bagher |
| contents | This study investigates brain connectivity and information flow during mental workload (MWL) by integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. Utilizing the N-back task to induce varying levels of MWL in 26 participants, we analyzed both functional and effective connectivity across 25 cortical regions derived from combined EEG and fNIRS signals. Functional connectivity was assessed using Pearson Correlation Coefficient (PCC), Phase Locking Value (PLV), and Magnitude Squared Coherence (MSC), while effective connectivity was evaluated using directed Directed Transfer Function (dDTF) and generalized Partial Directed Coherence (gPDC). Our findings reveal increased functional connectivity in frontal regions during higher MWL conditions (3-back compared to 0-back). Furthermore, effective connectivity analysis demonstrates a significant directional information flow from EEG to fNIRS, indicating a dominant influence of neural activity on hemodynamic responses. Statistical tests confirm significant differences in connectivity patterns between low and high MWL states. These results underscore the utility of EEG-fNIRS integration for characterizing brain network dynamics under varying cognitive demands and provide insights into neurovascular coupling mechanisms during mental workload. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Brain Connectivity and Information Flow in Mental Workload Using EEG and fNIRS Integration Qaremohammadlou, Mohaddese Shamsollahi, Mohammad Bagher Signal Processing This study investigates brain connectivity and information flow during mental workload (MWL) by integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. Utilizing the N-back task to induce varying levels of MWL in 26 participants, we analyzed both functional and effective connectivity across 25 cortical regions derived from combined EEG and fNIRS signals. Functional connectivity was assessed using Pearson Correlation Coefficient (PCC), Phase Locking Value (PLV), and Magnitude Squared Coherence (MSC), while effective connectivity was evaluated using directed Directed Transfer Function (dDTF) and generalized Partial Directed Coherence (gPDC). Our findings reveal increased functional connectivity in frontal regions during higher MWL conditions (3-back compared to 0-back). Furthermore, effective connectivity analysis demonstrates a significant directional information flow from EEG to fNIRS, indicating a dominant influence of neural activity on hemodynamic responses. Statistical tests confirm significant differences in connectivity patterns between low and high MWL states. These results underscore the utility of EEG-fNIRS integration for characterizing brain network dynamics under varying cognitive demands and provide insights into neurovascular coupling mechanisms during mental workload. |
| title | Investigating Brain Connectivity and Information Flow in Mental Workload Using EEG and fNIRS Integration |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.02457 |