2020
DOI: 10.1155/2020/7285057
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Abstract: Motor imagery brain-computer interfaces (BCIs) have demonstrated great potential and attract world-spread attentions. Due to the nonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This prevents them from going into our realistic life. In this paper, the source subject's data are explored to perform calibration for target subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to … Show more

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Cited by 11 publications
(7 citation statements)
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References 39 publications
(108 reference statements)
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“…RSVP systems ( Ratcliffe and Puthusserypady, 2020 ) were proposed to solve this problem, but the defect of these protocols was the long duration of the experiment time that deteriorates the ITR and lowers accuracy. Many scholars have done extensive research on improving the performance of BCI, such as creating a P300 speller performance predictor based on RSVP multifeature ( Won et al, 2019 ), finding the optimal features ( Yin et al, 2015 , 2016 ; Won et al, 2018 ), and using deep neural network algorithm ( Zhao et al, 2020 ), which cannot have a better result in a large subject study. In our study, the average ITR of the RSVP speller is 43.18 bits/min, which is 13% higher than the matrix paradigm, and we achieved the better accuracy results at present in a large subject study.…”
Section: Discussionmentioning
confidence: 99%
“…The most important detail was that subjects of this dataset were explicitly asked to imagine the kinesthetic experience rather than imagining the visual experience [26], [40]. Besides, an algorithm with high accuracy for a subject may perform terribly for other subjects, which is called a cross-subject problem [45]. Thus, this paper tested cross-subject accuracy.…”
Section: B Experimental Detailsmentioning
confidence: 99%
“…The most important detail was that subjects of this dataset were explicitly asked to imagine the kinesthetic experience rather than imagining the visual experience [16,23]. Besides, an algorithm with high accuracy for a subject could perform terribly for other subjects, which is called cross-subject problem [25]. Thus, this paper tested cross-subject accuracy.…”
Section: Subjectsmentioning
confidence: 99%
“…The most important detail was that subjects of this dataset were explicitly asked to imagine the kinesthetic experience rather than imagining the visual experience [32,37]. Besides, an algorithm with high accuracy for a subject could perform terribly for other subjects, which is called a cross-subject problem [39]. Thus, this paper tested cross-subject accuracy.…”
Section: (A) Subjectsmentioning
confidence: 99%
“…The most important detail was that subjects of this dataset were explicitly asked to imagine the kinesthetic experience rather than imagining the visual experience [25], [37]. Besides, an algorithm with high accuracy for a subject may perform terribly for other subjects, which is called a cross-subject problem [42]. Thus, this paper tested cross-subject accuracy.…”
Section: B Experimental Detailsmentioning
confidence: 99%