Exploring Deep Learning Models for EEG Neural Decoding
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913983844319232 |
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| author | Dixen, Laurits Heinrich, Stefan Burelli, Paolo |
| author_facet | Dixen, Laurits Heinrich, Stefan Burelli, Paolo |
| contents | Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models' performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_16567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Deep Learning Models for EEG Neural Decoding Dixen, Laurits Heinrich, Stefan Burelli, Paolo Machine Learning Signal Processing Neurons and Cognition Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models' performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity. |
| title | Exploring Deep Learning Models for EEG Neural Decoding |
| topic | Machine Learning Signal Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2503.16567 |