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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15018 |
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| _version_ | 1866909297608228864 |
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| author | Chen, Jun Chen, Anqi Jiang, Bingkun Obaidat, Mohammad S. Li, Ni Zhang, Xinyu |
| author_facet | Chen, Jun Chen, Anqi Jiang, Bingkun Obaidat, Mohammad S. Li, Ni Zhang, Xinyu |
| contents | Cognition refers to the function of information perception and processing, which is the fundamental psychological essence of human beings. It is responsible for reasoning and decision-making, while its evaluation is significant for the aviation domain in mitigating potential safety risks. Existing studies tend to use varied methods for cognitive state evaluation yet have limitations in timeliness, generalisation, and interpretability. Accordingly, this study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states. Specifically, a virtual reality-based flight platform is constructed with multi-screen embedded. Three distinctive cognitive tasks are designed and each has three degrees of difficulty. Thirty subjects are acquired for analysis and evaluation. The results are interpreted through different perspectives, including inner-subject and cross-subject for task-wise and gender-wise underlying brain functional connectivity. Additionally, this study incorporates questionnaire-based, task performance-based, and physiological measure-based approaches to fairly label the trials. A multi-class cognitive state evaluation is further conducted with the active brain connections. Benchmarking results demonstrate that the identified brain regions have considerable influences in cognition, with a multi-class accuracy rate of 95.83% surpassing existing studies. The derived findings bring significance to understanding the dynamic relationships among human brain functional regions, cross-subject cognitive behaviours, and decision-making, which have promising practical application values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15018 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation Chen, Jun Chen, Anqi Jiang, Bingkun Obaidat, Mohammad S. Li, Ni Zhang, Xinyu Human-Computer Interaction Artificial Intelligence Cognition refers to the function of information perception and processing, which is the fundamental psychological essence of human beings. It is responsible for reasoning and decision-making, while its evaluation is significant for the aviation domain in mitigating potential safety risks. Existing studies tend to use varied methods for cognitive state evaluation yet have limitations in timeliness, generalisation, and interpretability. Accordingly, this study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states. Specifically, a virtual reality-based flight platform is constructed with multi-screen embedded. Three distinctive cognitive tasks are designed and each has three degrees of difficulty. Thirty subjects are acquired for analysis and evaluation. The results are interpreted through different perspectives, including inner-subject and cross-subject for task-wise and gender-wise underlying brain functional connectivity. Additionally, this study incorporates questionnaire-based, task performance-based, and physiological measure-based approaches to fairly label the trials. A multi-class cognitive state evaluation is further conducted with the active brain connections. Benchmarking results demonstrate that the identified brain regions have considerable influences in cognition, with a multi-class accuracy rate of 95.83% surpassing existing studies. The derived findings bring significance to understanding the dynamic relationships among human brain functional regions, cross-subject cognitive behaviours, and decision-making, which have promising practical application values. |
| title | Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15018 |