Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138977
Title: Modeling EEG-based motor imagery with session to session online adaptation
Authors: Zhang, Zhuo
Foong, Ruyi
Phua, Kok Soon
Wang, Chuanchu
Ang, Kai Keng
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Zhang, Z., Foong, R., Phua, K. S., Wang, C., & Ang, K. K. (2018). Modeling EEG-based motor imagery with session to session online adaptation. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1988-1991. doi:10.1109/EMBC.2018.8512706
Abstract: Subject-specific calibration plays an important role in electroencephalography (EEG)-based Brain-Computer Interface (BCI) for Motor Imagery (MI) detection. A calibration session is often introduced to build a subject specific model, which then can be deployed into BCI system for MI detection in the following rehabilitation sessions. The model is termed as a fixed calibration model. Progressive adaptive models can also be built by using data not only from calibration session, but also from available rehabilitation sessions. It was reported that the progressive adaptive model yielded significant improved MI detection compared to the fixed model in a retrospective clinical study. In this work, we deploy the progressive adaptation model in a BCI-based stroke rehabilitation system and bring it online. We dub this system nBETTER (Neurostyle Brain Exercise Therapy Towards Enhanced Recovery). A clinical trial using the nBETTER system was conducted to evaluate the performance of 11 stroke patients who underwent a calibration session followed by 18 rehabilitation sessions over 6 weeks. We conduct retrospective analysis to compare the performance of various modeling strategies: the fixed calibration model, the online progressive adaptation model and a light-weight adaptation model, where the second one is generated online by nBETTER system and the other two models are obtained retrospectively. The mean accuracy of the three models across 11 subjects are 68.17%, 74.04% and 74.53% respectively. Statistical test conducted on the three groups using ANOVA yields a p-value of 9.83-e06. The test result shows that the two adaptation models both have significant different mean from fixed mode. Hence our study confirmed the effectiveness of using the progressive adaptive model for EEGbased BCI to detect MI in an online setting.
URI: https://hdl.handle.net/10356/138977
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512706
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC.2018.8512706
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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