Comparison of Classical EEG Source Analysis with Deep Learning
Abstract
This paper discusses the challenges and methods for source reconstruction of evoked potentials using deep learning in the context of electroencephalography (EEG). We propose the use of deep learning to address known challenges and improve traditional approaches. We explain the creation of a suitable dataset for solving the inverse problem, including the simulation of neural activity and the use of lead field matrices for the forward solution. Furthermore, we undertake a comparative analysis of some initial deep learning models with similar classical methods.
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| Titel | Comparison of Classical EEG Source Analysis with Deep Learning |
|---|---|
| Medien | In: Aguiar, A.P., Rocha Malonek, P., Pinto, V.H., Fontes, F.A.C.C., Chertovskih, R. (eds) CONTROLO 2024. CONTROLO 2024. Lecture Notes in Electrical Engineering, vol 1325. Springer |
| Verlag | Springer Nature Switzerland |
| Herausgeber | Springer Nature Switzerland |
| Band | 2025 |
| Verfasser | Jakob Winkler, Prof. Dr. Christian Uhl, Prof. Dr. Stefan Geißelsöder, Tim Erdbrügger, Carsten Wolters |
| Veröffentlichungsdatum | 23.04.2025 |
| Projekttitel | DyCA |
| Zitation | Winkler, Jakob; Uhl, Christian; Geißelsöder, Stefan; Erdbrügger, Tim; Wolters, Carsten (2025): Comparison of Classical EEG Source Analysis with Deep Learning. In: Aguiar, A.P., Rocha Malonek, P., Pinto, V.H., Fontes, F.A.C.C., Chertovskih, R. (eds) CONTROLO 2024. CONTROLO 2024. Lecture Notes in Electrical Engineering, vol 1325. Springer 2025. DOI: 10.1007/978-3-031-81724-3_24 |