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DC Field | Value | Language |
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dc.contributor.author | Zerafa, Rosanne | - |
dc.contributor.author | Camilleri, Tracey A. | - |
dc.contributor.author | Camilleri, Kenneth P. | - |
dc.date.accessioned | 2022-03-18T07:08:54Z | - |
dc.date.available | 2022-03-18T07:08:54Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Zerafa, R., Camilleri, T., & Camilleri, K. P. (2021, November). SAT: A Switch-And-Train Framework for Real-Time Training of SSVEP-based BCIs. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 959-962). IEEE. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/91739 | - |
dc.description.abstract | Reducing the training time for brain computer interfaces based on steady state evoked potentials, is essential to develop practical applications. We propose to eliminate the training required by the user before using the BCI with a switch-and-train (SAT) framework. Initially the BCI uses a training-free detection algorithm, and once sufficient training data is collected online, the BCI switches to a subject-specific training-based algorithm. Furthermore, the training-based algorithm is continuously re-trained in real-time. The performance of the SAT framework reached that of training-based algorithms for 8 out of 10 subjects after an average of 179 s ±33 s, an overall improvement over the training-free algorithm of 8.06%. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Biomedical engineering | en_GB |
dc.subject | Rehabilitation | en_GB |
dc.subject | Pattern recognition | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Optical data processing | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | SAT : a Switch-And-Train framework for real-time training of SSVEP-based BCIs | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | en_GB |
dc.bibliographicCitation.conferenceplace | Mexico,01-05/11/2021 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/EMBC46164.2021.9629488 | - |
Appears in Collections: | Scholarly Works - FacEngSCE |
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File | Description | Size | Format | |
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SAT_A_Switch-And-Train_Framework_for_Real-Time_Training_of_SSVEP-based_BCIs.pdf Restricted Access | 725.11 kB | Adobe PDF | View/Open Request a copy |
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