PRSEPTransformer-EEG: source-space sLORETA and residual transformers for robust motor imagery and execution EEG decoding

Decoding motor intentions from non-invasive scalp EEG remains a core challenge in brain–computer interface research, due to spatial blurring, low signal-to-noise ratio, and inter-subject variability. This work introduces PRSEPTransformer-EEG, a unified framework that integrates standardized preprocessing, source localization using sLORETA, and a deep learning model that combines squeeze–excitation residual blocks with a positional-encoding Transformer encoder. The framework is evaluated across three public datasets, encompassing both motor imagery and motor execution paradigms recorded via gel-based, water-based, and dry electrodes.

On the BCI Competition IV 2a dataset, the model achieves 99.53 % accuracy in the Beta band, surpassing the best previously reported results. On the BCI2000 four-class imagery dataset, it reaches 98.29 % in the Beta band and exceeds 92 % across all typical frequency ranges, outperforming existing source and sensor space models. Results from the reach-and-grasp dataset further confirm that source-space decoding offers substantial gains when high-density recordings are available, while sensor-level features remain effective in sparse configurations.

These findings demonstrate that combining source-space projection with temporally attentive spatial encoding can substantially advance the reliability of non-invasive EEG decoding for both clinical and assistive applications. Our code is available at (https://github.com/sinamakhdoomi/PRSEPTransfromerEEG.git). This research was funded by National Science Foundation (NSF) CAREER Award HCC-2053498 and NSF IUCRC BRAIN CNS-2333292.

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA Sina Makhdoomi Kaviri & Ramana Vinjamuri The authors declare no competiing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Kaviri, S.M., Vinjamuri, R. PRSEPTransformer-EEG: source-space sLORETA and residual transformers for robust motor imagery and execution EEG decoding.