Openly accessible

Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface

Hossain, Ibrahim, Khosravi, Abbas, Hettiarachchi, Imali and Nahavandi, Saeid 2018, Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface, Computational intelligence and neuroscience, vol. 2018, pp. 1-12, doi: 10.1155/2018/6323414.

Attached Files
Name Description MIMEType Size Downloads
nahavandi-multiclassinformative-2018.pdf Published version application/pdf 1.84MB 1

Title Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface
Author(s) Hossain, Ibrahim
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Hettiarachchi, ImaliORCID iD for Hettiarachchi, Imali orcid.org/0000-0002-4220-0970
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Computational intelligence and neuroscience
Volume number 2018
Article ID 6323414
Start page 1
End page 12
Total pages 12
Publisher Hindawi Publishing Corporation
Place of publication Cairo, Egypt
Publication date 2018-02-22
ISSN 1687-5273
Keyword(s) algorithms
brain
brain-computer interfaces
electroencephalography
electrooculography
imagination
motor activity
transfer (psychology)
science & technology
life sciences & biomedicine
mathematical & computational biology
neurosciences
neurosciences & neurology
Summary A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.
Language eng
DOI 10.1155/2018/6323414
Field of Research 1109 Neurosciences
1702 Cognitive Science
Copyright notice ©2018, Ibrahim Hossain et al.
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30107654

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 8 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Tue, 17 Apr 2018, 12:48:50 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.