Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction

Thomas C. Bulea; Saurabh Prasad; Atilla Kilicarslan; Jose L. Contreras-Vidal

doi:10.1109/EMBC.2013.6611004

Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction

Thomas C. Bulea, Saurabh Prasad, Atilla Kilicarslan, Jose L. Contreras-Vidal

Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.

Original language	English (US)
Title of host publication	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages	6341-6344
Number of pages	4
DOIs	https://doi.org/10.1109/EMBC.2013.6611004
State	Published - 2013
Event	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan Duration: Jul 3 2013 → Jul 7 2013

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Other

Other	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/Territory	Japan
City	Osaka
Period	7/3/13 → 7/7/13

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2013.6611004

Cite this

Bulea, T. C., Prasad, S., Kilicarslan, A., & Contreras-Vidal, J. L. (2013). Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 6341-6344). Article 6611004 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6611004

Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. / Bulea, Thomas C.; Prasad, Saurabh; Kilicarslan, Atilla et al.
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 6341-6344 6611004 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bulea, TC, Prasad, S, Kilicarslan, A & Contreras-Vidal, JL 2013, Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013., 6611004, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6341-6344, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, 7/3/13. https://doi.org/10.1109/EMBC.2013.6611004

Bulea TC, Prasad S, Kilicarslan A, Contreras-Vidal JL. Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 6341-6344. 6611004. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2013.6611004

Bulea, Thomas C. ; Prasad, Saurabh ; Kilicarslan, Atilla et al. / Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. pp. 6341-6344 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.",

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Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction

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