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Percentile range around the mean of center distance based informative transfer for motor imagery brain-computer interface
conference contribution
posted on 2018-01-01, 00:00 authored by Ibrahim HossainIbrahim Hossain, Abbas KhosraviAbbas Khosravi, Imali HettiarachchiImali Hettiarachchi, Saeid NahavandiAn ideal noninvasive electroencephalography (EEG) based brain-computer interface (BCI) is a user-friendly plug and play system where a new user does not need to go through the long training data collection process. To reduce the amount of training data required for a new user, active learning inspired informative instance transfer is investigated in this work as one of the potential solutions. In this informative transfer learning, query by committee is applied as query method to find informative samples from subjects own domain. On the other hand, percentile range around the mean of center distance (PRMCD) query method is introduced in this work as an alternative to existing entropy criterion to find informative samples from the past user's domain. The newly introduced PRMCD algorithm has reached the benchmark performance using only average 12% of whole subjective training set while the existing entropy-based algorithm has achieved the benchmark performance using average 17% of the whole subjective training set in case of 7 out of 9 subjects. For PRMCD algorithm, a new user can achieve the intended mean benchmark performance using reduced (only 50 which is 12.5%) amount of training data in general irrespective of subjects. Therefore, incorporation of PRMCD algorithm has added an important step towards the zero training BCI. It is a significant advancement for the practical application of motor imagery based BCI.
History
Event
International Neural Network Society. Conference (2018 : Rio de Janeiro, Brazil)Series
International Neural Network Society ConferencePagination
1 - 6Publisher
Institute of Electrical and Electronics EngineersLocation
Rio de Janeiro, BrazilPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-07-08End date
2018-07-13ISBN-13
9781509060146Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2018 : Proceedings of the 2018 International Joint Conference on Neural NetworksUsage metrics
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Keywords
TrainingEntropyElectroencephalographyTraining dataUncertaintyIntelligent systemsTechnological innovationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureEngineering, Electrical & ElectronicComputer ScienceEngineeringSimulation and Modelling
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