NDLI: Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI

Content Provider	IEEE Xplore Digital Library
Author	Battapady, H. Lin, P. Ding-Yu Fei Huang, D. Ou Bai
Copyright Year	2009
Description	Author affiliation: Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA (Battapady, H.; Ding-Yu Fei; Huang, D.; Ou Bai) \|\| Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders, National Institutes of Health, Bethesda, MD 20892, USA (Lin, P.)
Abstract	The objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Seven naïve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed. The multi-class classification for four-directional control was evaluated offline from 10-fold cross-validation using direct-decision tree classifier and genetic algorithm based Mahalanobis linear distance. Beta band (15–30Hz) event-related desynchronization and event related synchronization were observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed four-direction classification from SAM- filtered MEG signal was as high as 95–97% for physical movements and 86–87% for motor imagery. The high classification accuracy suggests that a reliable high performance two-dimensional BCI can be achieved from single trial detection of human natural movement intentions from SAM-filtered MEG signals, where user may not need extensive training.
Starting Page	524
Ending Page	527
File Size	604659
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333632
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Electroencephalography Decoding Communication system control Magnetic field measurement Classification tree analysis Muscles Diseases Brain Biomedical measurements
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals

Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Hand movement decoding by phase-locking low frequency EEG signals

Single trial independent component analysis for P300 BCI system

Movement decoding from noninvasive neural signals

Accuracy of a BCI based on movement-related and error potentials

Decoding Performance for Hand Movements: EEG vs. MEG

Multiclass classification of single-trial evoked EEG responses

Automatic detection and classification of artifacts in single-channel EEG

Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI

Similar Documents

Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals

Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Hand movement decoding by phase-locking low frequency EEG signals

Single trial independent component analysis for P300 BCI system

Movement decoding from noninvasive neural signals

Accuracy of a BCI based on movement-related and error potentials

Decoding Performance for Hand Movements: EEG vs. MEG

Multiclass classification of single-trial evoked EEG responses

Automatic detection and classification of artifacts in single-channel EEG

Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI