{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:52:36Z","timestamp":1781866356343,"version":"3.54.5"},"reference-count":40,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":148,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science, Research and Innovation Fund","award":["95618"],"award-info":[{"award-number":["95618"]}]},{"name":"National Science, Research and Innovation Fund","award":["179275"],"award-info":[{"award-number":["179275"]}]},{"name":"Research and Innovation Thailand","award":["B13F680099"],"award-info":[{"award-number":["B13F680099"]}]},{"DOI":"10.13039\/501100017170","name":"Thailand Science Research and Innovation","doi-asserted-by":"publisher","award":["FRB690039\/0457"],"award-info":[{"award-number":["FRB690039\/0457"]}],"id":[{"id":"10.13039\/501100017170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions during both motor execution (ME) and motor imagery (MI) tasks. Twenty-two healthy participants performed sitting and standing transitions under well-controlled experimental conditions while 60-channel EEG, electrooculography (EOG), and electromyography (EMG) signals were synchronously recorded. The dataset enables the exploration of neural activation patterns associated with lower-limb movements and supports the development of EEG-based brain\u2013computer interface (BCI) algorithms for mobility assistance and rehabilitation. To validate the dataset, benchmark classification was conducted on three baseline deep learning methods\u2014CTNet, EEGNet, and TCANet. Given the high inter-subject variability inherent to EEG, leave-one-subject-out cross-validation is used to ensure no subject bias during evaluation. Results demonstrated consistent decoding performance with mean accuracies of approximately 81% for ME and 73% for MI, indicating the reliability and usability of the dataset. Additionally, analyses of movement-related cortical potentials (MRCPs) and event-related desynchronization\/synchronization (ERD\/ERS) patterns revealed distinct neural signatures across the transition phases. This dataset provides a comprehensive foundation for studying lower-limb motor control, neural dynamics, and the advancement of MI-based BCIs for rehabilitation and assistive technologies.<\/jats:p>","DOI":"10.1093\/gigascience\/giag065","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T11:40:56Z","timestamp":1779968456000},"source":"Crossref","is-referenced-by-count":0,"title":["EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution"],"prefix":"10.1093","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7782-5856","authenticated-orcid":false,"given":"Benjakarn","family":"Uengsawapak","sequence":"first","affiliation":[{"name":"School of Information Science and Technology (IST), Vidyasirimedhi Institute of Science and Technology (VISTEC) , 555 Moo 1 Payupnai, Wangchan, Rayong 21210 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Institute of Technology, Thammasat University , 99 Moo 18, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120 ,","place":["Thailand"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4806-7576","authenticated-orcid":false,"given":"Poramate","family":"Manoonpong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology (IST), Vidyasirimedhi Institute of Science and Technology (VISTEC) , 555 Moo 1 Payupnai, Wangchan, Rayong 21210 ,","place":["Thailand"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7434-9262","authenticated-orcid":false,"given":"Gun","family":"Bhakdisongkhram","sequence":"additional","affiliation":[{"name":"School of Physical Medicine and Rehabilitation, Institute of Medicine, Suranaree University of Technology , 111 University Avenue, Suranaree, Muang, Nakhon Ratchasima 30000 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