Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908366869102592 |
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| author | Wang, Jiaheng Wang, Zhenyu Xu, Tianheng Si, Yuan Li, Ang Zhou, Ting Zhao, Xi Hu, Honglin |
| author_facet | Wang, Jiaheng Wang, Zhenyu Xu, Tianheng Si, Yuan Li, Ang Zhou, Ting Zhao, Xi Hu, Honglin |
| contents | As a method to connect human brain and external devices, Brain-computer interfaces (BCIs) are receiving extensive research attention. Recently, the integration of communication theory with BCI has emerged as a popular trend, offering potential to enhance system performance and shape next-generation communications.
A key challenge in this field is modeling the brain wireless communication channel between intracranial electrocorticography (ECoG) emitting neurons and extracranial electroencephalography (EEG) receiving electrodes. However, the complex physiology of brain challenges the application of traditional channel modeling methods, leaving relevant research in its infancy. To address this gap, we propose a frequency-division multiple-input multiple-output (MIMO) estimation framework leveraging simultaneous macaque EEG and ECoG recordings, while employing neurophysiology-informed regularization to suppress noise interference. This approach reveals profound similarities between neural signal propagation and multi-antenna communication systems. Experimental results show improved estimation accuracy over conventional methods while highlighting a trade-off between frequency resolution and temporal stability determined by signal duration. This work establish a conceptual bridge between neural interfacing and communication theory, accelerating synergistic developments in both fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling Wang, Jiaheng Wang, Zhenyu Xu, Tianheng Si, Yuan Li, Ang Zhou, Ting Zhao, Xi Hu, Honglin Signal Processing Human-Computer Interaction As a method to connect human brain and external devices, Brain-computer interfaces (BCIs) are receiving extensive research attention. Recently, the integration of communication theory with BCI has emerged as a popular trend, offering potential to enhance system performance and shape next-generation communications. A key challenge in this field is modeling the brain wireless communication channel between intracranial electrocorticography (ECoG) emitting neurons and extracranial electroencephalography (EEG) receiving electrodes. However, the complex physiology of brain challenges the application of traditional channel modeling methods, leaving relevant research in its infancy. To address this gap, we propose a frequency-division multiple-input multiple-output (MIMO) estimation framework leveraging simultaneous macaque EEG and ECoG recordings, while employing neurophysiology-informed regularization to suppress noise interference. This approach reveals profound similarities between neural signal propagation and multi-antenna communication systems. Experimental results show improved estimation accuracy over conventional methods while highlighting a trade-off between frequency resolution and temporal stability determined by signal duration. This work establish a conceptual bridge between neural interfacing and communication theory, accelerating synergistic developments in both fields. |
| title | Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling |
| topic | Signal Processing Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.10786 |