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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.07843 |
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| _version_ | 1866911267200958464 |
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| author | Lee, Chae-Won Lee, Jong-Seok |
| author_facet | Lee, Chae-Won Lee, Jong-Seok |
| contents | In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively consider inter-regional interaction patterns in EEG. However, we find that such an approach has a limitation that important patterns in connectivity matrices may be lost during the convolutional operations in CNNs. To resolve this issue, we propose and validate an idea to upscale the connectivity matrices to strengthen the local patterns. Experimental results demonstrate that this simple idea can significantly enhance the classification performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emotional EEG Classification using Upscaled Connectivity Matrices Lee, Chae-Won Lee, Jong-Seok Machine Learning In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively consider inter-regional interaction patterns in EEG. However, we find that such an approach has a limitation that important patterns in connectivity matrices may be lost during the convolutional operations in CNNs. To resolve this issue, we propose and validate an idea to upscale the connectivity matrices to strengthen the local patterns. Experimental results demonstrate that this simple idea can significantly enhance the classification performance. |
| title | Emotional EEG Classification using Upscaled Connectivity Matrices |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.07843 |