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Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs
Wong,Chi Man1,2; Wan,Feng1,2; Wang,Boyu3; Wang,Ze1,2; Nan,Wenya4; Lao,Ka Fai1; Mak,Peng Un1; Vai,Mang I.1,5; Rosa,Agostinho6
Source PublicationJournal of Neural Engineering
AbstractObjective. Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.
Keywordbrain-computer interface canonical correlation analysis learning across multi-stimulus steady-state visual evoked potential task-related component analysis
URLView the original
Scopus ID2-s2.0-85077668605
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Cited Times [WOS]:35   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorWan,Feng
Affiliation1.Department of Electrical and Computer Engineering,Faculty of Science and Technology,University of Macau,Macao
2.Centre for Cognitive and Brain Sciences,Institute of Collaborative Innovation,University of Macau,Macao
3.Department of Computer Science,University of Western Ontario,London,Canada
4.Department of Psychology,Shanghai Normal University,Shanghai,China
5.State Key Laboratory of Analog and Mixed-Signal VLSI,University of Macau,Macao
6.LaSEEB-ISR-LARSyS,Universidade de Lisboa,Lisbon,Portugal
First Author AffilicationFaculty of Science and Technology;  University of Macau
Corresponding Author AffilicationFaculty of Science and Technology;  University of Macau
Recommended Citation
GB/T 7714
Wong,Chi Man,Wan,Feng,Wang,Boyu,et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs[J]. Journal of Neural Engineering,2020,17(1).
APA Wong,Chi Man,Wan,Feng,Wang,Boyu,Wang,Ze,Nan,Wenya,Lao,Ka Fai,Mak,Peng Un,Vai,Mang I.,&Rosa,Agostinho.(2020).Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs.Journal of Neural Engineering,17(1).
MLA Wong,Chi Man,et al."Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs".Journal of Neural Engineering 17.1(2020).
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