{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T05:07:11Z","timestamp":1789189631662,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Remote electroencephalography (EEG) systems require compact representations that remain useful when communication noise corrupts the transmitted message. We evaluated whether task-oriented residual refinement of filter bank common spatial pattern (FBCSP) features compressed by principal component analysis (PCA) improves four-class motor imagery (MI) decoding without increasing the transmitted dimension. The BNCI2014-001 dataset was assessed in nine subjects using bidirectional subject-specific cross-session evaluation. All methods transmitted K\u2208{16,32,64} power-normalized real values through additive white Gaussian noise (AWGN) at seven signal-to-noise ratios (SNRs) and a noise-free reference. Balanced accuracy was averaged over 20 paired noise realizations per noisy condition, and paired subject-level differences were evaluated with exact joint sign-flip max-|t| inference. At K=32, the proposed method achieved 41.10%, 49.46%, and 56.15% balanced accuracy at \u221210, \u22125, and 0 dB, compared with 38.63%, 46.28%, and 53.26% for conventional FBCSP\u2013PCA transmission. Ten of the 24 semantic-versus-conventional comparisons were significant after family-wise max-|t| correction, including all nine comparisons at \u221210, \u22125, and 0 dB. Receiver-only controls closely reproduced conventional performance at all three message dimensions, whereas alternative loss weights, uniform-SNR training and selection, and removal of the 0 dB\/noise-free reference-condition guard retained positive low-SNR gains. Overall, baseline-preserving task-oriented refinement improved MI decision robustness under severe AWGN without increasing the number of transmitted values.<\/jats:p>","DOI":"10.3390\/s26185728","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:02:48Z","timestamp":1788962568000},"page":"5728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Task-Oriented Semantic Feature Transmission for Robust EEG Motor Imagery Decoding Under Additive White Gaussian Noise"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3650-0280","authenticated-orcid":false,"given":"Hossein","family":"Ahmadi","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8239-2348","authenticated-orcid":false,"given":"Luca","family":"Mesin","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. 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