{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T08:15:37Z","timestamp":1763021737158,"version":"3.45.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2025,11,13]]},"abstract":"<jats:p>Monitoring pilots\u2019 mental states during training is important for ensuring safety and optimizing performance, yet existing full-cap EEG systems are impractical for operational use. In this work, we evaluate a headphone-style 9-channel electrode montage rather than a complete headphone EEG device, and propose a transfer learning framework to preserve performance under sparse coverage. Our two-stage approach first applies self-supervised pretraining on 64-channel full-cap EEG, and then adapts the learned representations to the headphone-style montage through a position correction module that accounts for electrode misplacement. We validate the framework in a flight simulator with 12 participants on two tasks: motor intention (left vs. right) and cognitive workload (low vs. high). Despite limited coverage, the headphone-style montage achieves within-subject accuracies of 85% for motor imagery and 89% for workload, compared to full-cap performance of 88% and 91%. Cross-subject accuracies reached 66% and 73%, demonstrating generalizability across users. Ablation analyses show that both self-supervised pretraining and position correction independently improve performance, and together provide a 12% boost in cross-subject decoding accuracy. These findings highlight the feasibility of headphone-style EEG montages for practical pilot monitoring, while clarifying methodological limitations and the need for future real-time and user-centered evaluation.<\/jats:p>","DOI":"10.1145\/3773070","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T08:12:18Z","timestamp":1763021538000},"page":"277-291","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG-Based Monitoring of Pilot Training: Transferring Full-Cap Representations to Headphone-Style Electrode Positions"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6109-5838","authenticated-orcid":false,"given":"Linyu","family":"Zheng","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-0283","authenticated-orcid":false,"given":"Yujing Mark","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4701-7408","authenticated-orcid":false,"given":"Zhuo","family":"Liu","sequence":"additional","affiliation":[{"name":"AVIC General Huanan Aircraft Industry, Zhuhai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3105-5269","authenticated-orcid":false,"given":"Weidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 985\u2013989","author":"An Winko W","year":"2021","unstructured":"Winko W An, Barbara Shinn-Cunningham, Hannes Gamper, Dimitra Emmanouilidou, David Johnston, Mihai Jalobeanu, Edward Cutrell, Andrew Wilson, Kuan-Jung Chiang, and Ivan Tashev. 2021. 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