Abstract
Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse problem. While deep learning has advanced spatial reconstruction, current architectures face a critical dilemma: frame-by-frame models discard vital temporal context, whereas full 4D spatiotemporal networks introduce an architectural trade-off between reconstruction accuracy and inference cost. We propose a novel two-stream framework that explicitly decouples global temporal representation learning from per-time-point spatial refinement. A Transformer-based Temporal Condition Encoder processes the entire EEG sequence via factorized spatiotemporal attention, retaining sensor-resolved features. A fixed inverse then maps these features into source-indexed conditioning for a per-timestep Source-Space Transformer or volumetric convolutional refiner. Extensive evaluations on realistic synthetic data demonstrate that this temporal prior dramatically improves spatial localization, outperforming classical and spatiotemporal baselines, particularly in high-noise and multi-source regimes. Training across diverse leadfields and explicit operator mismatches improves transfer to unseen head geometries and brings template-based reconstruction closer to subject-specific inversion. Furthermore, we apply the model trained only on synthetic EEG data to real-world EEG. A logistic regressor fit on source power differences in eyes-open, eyes-closed conditions successfully decodes age groups.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Morik, M., Palarus, J., Vidaurre, C., Müller, K. R., & Nakajima, S. (2026). Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging. https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging
MLA 9
Morik, Marco, et al. "Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging." https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging.
Chicago (author–date)
Morik, Marco, Jesse Palarus, Carmen Vidaurre, Klaus-Robert Müller, and Shinichi Nakajima. 2026. "Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging." https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging.
Harvard
Morik, M., Palarus, J., Vidaurre, C., Müller, K. R. and Nakajima, S. (2026) 'Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging', Available at: https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging.
Vancouver
Morik M, Palarus J, Vidaurre C, Müller KR, Nakajima S. Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging. https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging
IEEE
M. Morik, J. Palarus, C. Vidaurre, K. R. Müller, and S. Nakajima, "Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging," https://omanscience.com/en/articles/decoupling-time-and-space-a-temporally-conditioned-refinement-for-eeg-source-imaging.