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However, the specific cortical regions involved remain unclear. Here, we conducted a whole-cortex analysis of alpha-band changes by examining source-level electroencephalographic (EEG) signals during a cued visuospatial attention task via a novel approach integrating conventional alpha power analysis with a deep learning technique based on an interpretable convolutional neural network (CNN). Conventional metrics contrasting alpha power between leftward and rightward attention suggested a robust, selective involvement of the left parietal lobe (superior and inferior parietal cortices) and a broader involvement of right hemisphere regions with less parietal contribution. The CNN-based approach, which discriminated the attention direction from the source-level EEG signals and identified the most discriminative regions in alpha band, refined these findings, corroborating the dominant role of the left parietal lobe and limited involvement of the right parietal lobe restricted to the supramarginal gyrus. The obtained findings are interpreted in terms of a more tonic engagement (disinhibition) of right parietal lobe, leaving less dynamic range for condition-dependent alpha modulation. This study not only improves the characterization of alpha-band attention-related changes but also presents a novel combined approach to investigate brain oscillations.<\/jats:p>","DOI":"10.1093\/cercor\/bhag026","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:18:15Z","timestamp":1774505895000},"source":"Crossref","is-referenced-by-count":1,"title":["Cortical alpha changes during visuospatial attention: a deep learning\u2013enriched EEG analysis"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4673-2974","authenticated-orcid":false,"given":"Elisa","family":"Magosso","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d (DEI), University of Bologna, Cesena Campus , Via dell'Universit\u00e0 50, 47521 Cesena (FC) ,","place":["Italy"]},{"name":"Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna , Via Galliera 3, 40121 Bologna ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3791-8555","authenticated-orcid":false,"given":"Davide","family":"Borra","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d (DEI), University of Bologna, Cesena Campus , Via dell'Universit\u00e0 50, 47521 Cesena (FC) ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"2026032602181260200_ref1","doi-asserted-by":"publisher","DOI":"10.71568\/dasdaga2025.350","volume-title":"Classifying the direction of auditory attention from EEG signals using deep learning with a short decision 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