{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:01:32Z","timestamp":1760148092094,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Graz University of Technology","award":["ERC-CoG-2015 681231"],"award-info":[{"award-number":["ERC-CoG-2015 681231"]}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-CoG-2015 681231"],"award-info":[{"award-number":["ERC-CoG-2015 681231"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Royal Thai Government","award":["ERC-CoG-2015 681231"],"award-info":[{"award-number":["ERC-CoG-2015 681231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain\u2013computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder\u2019s incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.<\/jats:p>","DOI":"10.3390\/s23073593","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T02:23:46Z","timestamp":1680143026000},"page":"3593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transfer Learning in Trajectory Decoding: Sensor or Source Space?"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9541-2777","authenticated-orcid":false,"given":"Nitikorn","family":"Srisrisawang","sequence":"first","affiliation":[{"name":"Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16\/IV, 8010 Graz, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0087-3720","authenticated-orcid":false,"given":"Gernot R.","family":"M\u00fcller-Putz","sequence":"additional","affiliation":[{"name":"Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16\/IV, 8010 Graz, Austria"},{"name":"BioTechMed Graz, 8010 Graz, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","article-title":"Event-Related EEG\/MEG Synchronization and Desynchronization: Basic Principles","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. 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