{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T14:48:59Z","timestamp":1768747739522,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T00:00:00Z","timestamp":1547683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001554","name":"Massey University","doi-asserted-by":"publisher","award":["RM20730"],"award-info":[{"award-number":["RM20730"]}],"id":[{"id":"10.13039\/501100001554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalogram (EEG) based motor imagery brain\u2013computer interface (BCI) requires large number of subject specific training trials to calibrate the system for a new subject. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of a brain\u2013computer interface is to reduce calibration time or completely eliminate it. To address this problem, existing approaches use covariance matrices of electroencephalography (EEG) trials as descriptors for decoding BCI but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing SPD based classification approach. However, SPD-based classification has limited applicability in small training sets because the dimensionality of covariance matrices is large in proportion to the number of trials. To overcome this drawback, our paper proposes a new framework that transforms SPD matrices in lower dimension through spatial filter regularized by prior information of EEG channels. The efficacy of the proposed approach was validated on the small sample scenario through Dataset IVa from BCI Competition III. The proposed approach achieved mean accuracy of     86.13 %     and mean kappa of     0.72     on Dataset IVa. The proposed method outperformed other approaches in existing studies on Dataset IVa. Finally, to ensure the robustness of the proposed method, we evaluated it on Dataset IIIa from BCI Competition III and Dataset IIa from BCI Competition IV. The proposed method achieved mean accuracy     92.22 %     and     81.21 %     on Dataset IIIa and Dataset IIa, respectively.<\/jats:p>","DOI":"10.3390\/s19020379","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T02:22:23Z","timestamp":1547778143000},"page":"379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Reduce Calibration Time in Motor Imagery Using Spatially Regularized Symmetric Positives-Definite Matrices Based Classification"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1916-3347","authenticated-orcid":false,"given":"Amardeep","family":"Singh","sequence":"first","affiliation":[{"name":"School of Fundamental Sciences, Massey University, Manawatu Private Bag 11 222, Palmerston North 4442, New Zealand"}]},{"given":"Sunil","family":"Lal","sequence":"additional","affiliation":[{"name":"School of Fundamental Sciences, Massey University, Manawatu Private Bag 11 222, Palmerston North 4442, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8160-5946","authenticated-orcid":false,"given":"Hans W.","family":"Guesgen","sequence":"additional","affiliation":[{"name":"School of Fundamental Sciences, Massey University, Manawatu Private Bag 11 222, Palmerston North 4442, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, A., Lal, S., and Guesgen, H.W. 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