NDLI: Predicting Inter-session Performance of SMR-Based Brain–Computer Interface Using the Spectral Entropy of Resting-State EEG

Content Provider	Springer Nature Link
Author	Zhang, Rui Xu, Peng Chen, Rui Li, Fali Guo, Lanjin Li, Peiyang Zhang, Tao Yao, Dezhong
Copyright Year	2015
Abstract	Currently most subjects can control the sensorimotor rhythm-based brain–computer interface (SMR-BCI) successfully after several training procedures. However, 15–30 % of subjects cannot achieve SMR-BCI control even after long-term training, and they are termed as “BCI inefficiency”. This study focuses on the investigation of reliable SMR-BCI performance predictor. 40 subjects participated in the first experimental session and 26 of them returned in the second session, each session consists of an eyes closed/open resting-state EEG recording run and four EEG recording runs with hand motor imagery. We found spectral entropy derived from eyes closed resting-state EEG of channel C3 has a high correlation with SMR-BCI performance (r = 0.65). Thus, we proposed to use it as a biomarker to predict individual SMR-BCI performance. Receiver operating characteristics analysis and leave-one-out cross-validation demonstrated that the spectral entropy predictor provide outstanding classification capability for high and low aptitude BCI users. To our knowledge, there has been no discussion about the reliability of inter-session prediction in previous studies. We further evaluated the inter-session prediction performance of the spectral entropy predictor, and the results showed that the average classification accuracy of inter-session prediction up to 89 %. The proposed predictor is convenient to obtain because it derived from single channel resting-state EEG, it could be used to identify potential SMR-BCI inefficiency subjects from novel users. But there are still limitations because Kübler et al. have shown that some BCI users may need eight or more sessions before they develop classifiable SMR activity.
Starting Page	680
Ending Page	690
Page Count	11
File Format	PDF
ISSN	08960267
Journal	Brain Topography
Volume Number	28
Issue Number	5
e-ISSN	15736792
Language	English
Publisher	Springer US
Publisher Date	2015-03-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Brain–machine interface (BMI) Resting-state EEG BCI inefficiency Biomarker Classification Neurosciences Psychiatry Neurology
Content Type	Text
Resource Type	Article
Subject	Neurology Anatomy Radiology, Nuclear Medicine and Imaging Neurology (clinical) Radiological and Ultrasound Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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