Enhancing Subject-Independent Accuracy in fNIRS-based Brain-Computer Interfaces with Optimized Channel Selection
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915173947670528 |
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| author | Li, Yuxin Fang, Hao Liu, Wen Cheng, Chuantong Chen, Hongda |
| author_facet | Li, Yuxin Fang, Hao Liu, Wen Cheng, Chuantong Chen, Hongda |
| contents | Achieving high subject-independent accuracy in functional near-infrared spectroscopy (fNIRS)-based brain-computer interfaces (BCIs) remains a challenge, particularly when minimizing the number of channels. This study proposes a novel feature extraction scheme and a Pearson correlation-based channel selection algorithm to enhance classification accuracy while reducing hardware complexity. Using an open-access fNIRS dataset, our method improved average accuracy by 28.09% compared to existing approaches, achieving a peak subject-independent accuracy of 95.98% with only two channels. These results demonstrate the potential of our optimized feature extraction and channel selection methods for developing efficient, subject-independent fNIRS-based BCI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Subject-Independent Accuracy in fNIRS-based Brain-Computer Interfaces with Optimized Channel Selection Li, Yuxin Fang, Hao Liu, Wen Cheng, Chuantong Chen, Hongda Human-Computer Interaction Signal Processing Achieving high subject-independent accuracy in functional near-infrared spectroscopy (fNIRS)-based brain-computer interfaces (BCIs) remains a challenge, particularly when minimizing the number of channels. This study proposes a novel feature extraction scheme and a Pearson correlation-based channel selection algorithm to enhance classification accuracy while reducing hardware complexity. Using an open-access fNIRS dataset, our method improved average accuracy by 28.09% compared to existing approaches, achieving a peak subject-independent accuracy of 95.98% with only two channels. These results demonstrate the potential of our optimized feature extraction and channel selection methods for developing efficient, subject-independent fNIRS-based BCI systems. |
| title | Enhancing Subject-Independent Accuracy in fNIRS-based Brain-Computer Interfaces with Optimized Channel Selection |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2502.18719 |