State and trajectory decoding of upper extremity movements from electrocorticogram

Po T. Wang; Eric J. Puttock; Christine E. King; Andrew Schombs; Jack J. Lin; Mona Sazgar; Frank P.K. Hsu; Susan J. Shaw; David E. Millett; Charles Y. Liu; Luis A. Chui; An H. Do; Zoran Nenadic

doi:10.1109/NER.2013.6696097

State and trajectory decoding of upper extremity movements from electrocorticogram

Po T. Wang, Eric J. Puttock, Christine E. King, Andrew Schombs, Jack J. Lin, Mona Sazgar, Frank P.K. Hsu, Susan J. Shaw, David E. Millett, Charles Y. Liu, Luis A. Chui, An H. Do, Zoran Nenadic

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

9 Scopus citations

Abstract

Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.

Original language	English (US)
Title of host publication	2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages	969-972
Number of pages	4
DOIs	https://doi.org/10.1109/NER.2013.6696097
State	Published - 2013
Event	2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States Duration: Nov 6 2013 → Nov 8 2013

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Other

Other	2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Country/Territory	United States
City	San Diego, CA
Period	11/6/13 → 11/8/13

ASJC Scopus subject areas

Artificial Intelligence
Mechanical Engineering

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10.1109/NER.2013.6696097

Cite this

Wang, P. T., Puttock, E. J., King, C. E., Schombs, A., Lin, J. J., Sazgar, M., Hsu, F. P. K., Shaw, S. J., Millett, D. E., Liu, C. Y., Chui, L. A., Do, A. H., & Nenadic, Z. (2013). State and trajectory decoding of upper extremity movements from electrocorticogram. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 (pp. 969-972). Article 6696097 (International IEEE/EMBS Conference on Neural Engineering, NER). https://doi.org/10.1109/NER.2013.6696097

State and trajectory decoding of upper extremity movements from electrocorticogram. / Wang, Po T.; Puttock, Eric J.; King, Christine E. et al.
2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013. 2013. p. 969-972 6696097 (International IEEE/EMBS Conference on Neural Engineering, NER).

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

Wang, PT, Puttock, EJ, King, CE, Schombs, A, Lin, JJ, Sazgar, M, Hsu, FPK, Shaw, SJ, Millett, DE, Liu, CY, Chui, LA, Do, AH & Nenadic, Z 2013, State and trajectory decoding of upper extremity movements from electrocorticogram. in 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013., 6696097, International IEEE/EMBS Conference on Neural Engineering, NER, pp. 969-972, 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013, San Diego, CA, United States, 11/6/13. https://doi.org/10.1109/NER.2013.6696097

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title = "State and trajectory decoding of upper extremity movements from electrocorticogram",

abstract = "Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.",

author = "Wang, {Po T.} and Puttock, {Eric J.} and King, {Christine E.} and Andrew Schombs and Lin, {Jack J.} and Mona Sazgar and Hsu, {Frank P.K.} and Shaw, {Susan J.} and Millett, {David E.} and Liu, {Charles Y.} and Chui, {Luis A.} and Do, {An H.} and Zoran Nenadic",

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AU - Wang, Po T.

AU - Puttock, Eric J.

AU - King, Christine E.

AU - Schombs, Andrew

AU - Lin, Jack J.

AU - Sazgar, Mona

AU - Hsu, Frank P.K.

AU - Shaw, Susan J.

AU - Millett, David E.

AU - Liu, Charles Y.

AU - Chui, Luis A.

AU - Do, An H.

AU - Nenadic, Zoran

PY - 2013

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N2 - Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.

AB - Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.

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BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013

T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013

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ER -

State and trajectory decoding of upper extremity movements from electrocorticogram

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