How Light Shapes Memory: Beta Synchrony in the Temporal-Parietal Cortex Predicts Cognitive Ergonomics for BCI Applications
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917157901697024 |
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| author | Li, Jiajia Guo, Tian Li, Fan Ding, Huichao Xu, Guozheng Song, Jian |
| author_facet | Li, Jiajia Guo, Tian Li, Fan Ding, Huichao Xu, Guozheng Song, Jian |
| contents | Working memory is a promising paradigm for assessing cognitive ergonomics of brain states in brain-computer interfaces(BCIs). This study decodes these states with a focus on environmental illumination effects via two distinct working memory tasks(Recall and Sequence) for mixed-recognition analysis. Leveraging nonlinear patterns in brain connectivity, we propose an innovative framework: multi-regional dynamic interplay patterns based on beta phase synchrony dynamics, to identify low-dimensional EEG regions (prefrontal, temporal, parietal) for state recognition. Based on nonlinear phase map analysis of the above three brain regions using beta-phase connectivity, we found that: (1)Temporal-parietal phase clustering outperforms other regional combinations in distinguishing memory states; (2)Illumination-enhanced environments optimize temporoparietal balance;(3) Machine learning confirms temporal-parietal synchrony as the dominant cross-task classification feature. These results provide a precise prediction algorithm, facilitating a low-dimensional system using temporal and parietal EEG channels with practical value for real-time cognitive ergonomics assessment in BCIs and optimized human-machine interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Light Shapes Memory: Beta Synchrony in the Temporal-Parietal Cortex Predicts Cognitive Ergonomics for BCI Applications Li, Jiajia Guo, Tian Li, Fan Ding, Huichao Xu, Guozheng Song, Jian Neurons and Cognition Working memory is a promising paradigm for assessing cognitive ergonomics of brain states in brain-computer interfaces(BCIs). This study decodes these states with a focus on environmental illumination effects via two distinct working memory tasks(Recall and Sequence) for mixed-recognition analysis. Leveraging nonlinear patterns in brain connectivity, we propose an innovative framework: multi-regional dynamic interplay patterns based on beta phase synchrony dynamics, to identify low-dimensional EEG regions (prefrontal, temporal, parietal) for state recognition. Based on nonlinear phase map analysis of the above three brain regions using beta-phase connectivity, we found that: (1)Temporal-parietal phase clustering outperforms other regional combinations in distinguishing memory states; (2)Illumination-enhanced environments optimize temporoparietal balance;(3) Machine learning confirms temporal-parietal synchrony as the dominant cross-task classification feature. These results provide a precise prediction algorithm, facilitating a low-dimensional system using temporal and parietal EEG channels with practical value for real-time cognitive ergonomics assessment in BCIs and optimized human-machine interaction. |
| title | How Light Shapes Memory: Beta Synchrony in the Temporal-Parietal Cortex Predicts Cognitive Ergonomics for BCI Applications |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2512.17775 |