Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910699185242112 |
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| author | Mehta, Rishan Rajpura, Param Meena, Yogesh Kumar |
| author_facet | Mehta, Rishan Rajpura, Param Meena, Yogesh Kumar |
| contents | Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalization by integrating resting-state EEG, aiming to improve motor imagery BCI performance and develop a user-generalized model. Using feature concatenation, we combine the EEGNet model, a standard convolutional neural network for EEG signal classification, with functional connectivity measures derived from resting-state EEG data. The findings suggest that although grounded in neuroscience with data-driven learning, the concatenation approach has limited benefits for generalizing models in within-user and across-user scenarios. While an improvement in mean accuracy for within-user scenarios is observed on two datasets, concatenation doesn't benefit across-user scenarios when compared with random data concatenation. The findings indicate the necessity of further investigation on the model interpretability and the effect of random data concatenation on model robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can EEG resting state data benefit data-driven approaches for motor-imagery decoding? Mehta, Rishan Rajpura, Param Meena, Yogesh Kumar Signal Processing Human-Computer Interaction Machine Learning Neurons and Cognition Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalization by integrating resting-state EEG, aiming to improve motor imagery BCI performance and develop a user-generalized model. Using feature concatenation, we combine the EEGNet model, a standard convolutional neural network for EEG signal classification, with functional connectivity measures derived from resting-state EEG data. The findings suggest that although grounded in neuroscience with data-driven learning, the concatenation approach has limited benefits for generalizing models in within-user and across-user scenarios. While an improvement in mean accuracy for within-user scenarios is observed on two datasets, concatenation doesn't benefit across-user scenarios when compared with random data concatenation. The findings indicate the necessity of further investigation on the model interpretability and the effect of random data concatenation on model robustness. |
| title | Can EEG resting state data benefit data-driven approaches for motor-imagery decoding? |
| topic | Signal Processing Human-Computer Interaction Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2411.09789 |