Motor Imagery Task Classification in EEG Signals with Spiking Neural Network

Carlos D. Virgilio G; Humberto Sossa; Javier M. Antelis; Luis E. Falcón

doi:10.1007/978-3-030-21077-9_2

Motor Imagery Task Classification in EEG Signals with Spiking Neural Network

Carlos D. Virgilio G, Humberto Sossa, Javier M. Antelis, Luis E. Falcón

Centro de Investigación en Computación (CIC)

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

5 Scopus citations

Abstract

We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network.

Original language	English
Title of host publication	Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings
Editors	Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López, Joaquín Salas
Publisher	Springer Verlag
Pages	14-24
Number of pages	11
ISBN (Print)	9783030210762
DOIs	https://doi.org/10.1007/978-3-030-21077-9_2
State	Published - 2019
Event	11th Mexican Conference on Pattern Recognition, MCPR 2019 - Querétaro, Mexico Duration: 26 Jun 2019 → 29 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11524 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Mexican Conference on Pattern Recognition, MCPR 2019
Country/Territory	Mexico
City	Querétaro
Period	26/06/19 → 29/06/19

Keywords

EEG signals
Motor Imagery
Multi layer perceptron
Neural networks
Power Spectral Density
Spiking Neural Network
Wavelet Decomposition

Access to Document

10.1007/978-3-030-21077-9_2

Cite this

Virgilio G, C. D., Sossa, H., Antelis, J. M., & Falcón, L. E. (2019). Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. In J. A. Carrasco-Ochoa, J. F. Martínez-Trinidad, J. A. Olvera-López, & J. Salas (Eds.), Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings (pp. 14-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11524 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-21077-9_2

Virgilio G, Carlos D. ; Sossa, Humberto ; Antelis, Javier M. et al. / Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings. editor / Jesús Ariel Carrasco-Ochoa ; José Francisco Martínez-Trinidad ; José Arturo Olvera-López ; Joaquín Salas. Springer Verlag, 2019. pp. 14-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{483bb98a3bd4490e9f7dd90cc1ea82dc,

title = "Motor Imagery Task Classification in EEG Signals with Spiking Neural Network",

abstract = "We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network.",

keywords = "EEG signals, Motor Imagery, Multi layer perceptron, Neural networks, Power Spectral Density, Spiking Neural Network, Wavelet Decomposition",

author = "{Virgilio G}, {Carlos D.} and Humberto Sossa and Antelis, {Javier M.} and Falc{\'o}n, {Luis E.}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 11th Mexican Conference on Pattern Recognition, MCPR 2019 ; Conference date: 26-06-2019 Through 29-06-2019",

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editor = "Carrasco-Ochoa, {Jes{\'u}s Ariel} and Mart{\'i}nez-Trinidad, {Jos{\'e} Francisco} and Olvera-L{\'o}pez, {Jos{\'e} Arturo} and Joaqu{\'i}n Salas",

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Virgilio G, CD, Sossa, H, Antelis, JM & Falcón, LE 2019, Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. in JA Carrasco-Ochoa, JF Martínez-Trinidad, JA Olvera-López & J Salas (eds), Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11524 LNCS, Springer Verlag, pp. 14-24, 11th Mexican Conference on Pattern Recognition, MCPR 2019, Querétaro, Mexico, 26/06/19. https://doi.org/10.1007/978-3-030-21077-9_2

Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. / Virgilio G, Carlos D.; Sossa, Humberto; Antelis, Javier M. et al.
Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings. ed. / Jesús Ariel Carrasco-Ochoa; José Francisco Martínez-Trinidad; José Arturo Olvera-López; Joaquín Salas. Springer Verlag, 2019. p. 14-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11524 LNCS).

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

TY - GEN

T1 - Motor Imagery Task Classification in EEG Signals with Spiking Neural Network

AU - Virgilio G, Carlos D.

AU - Sossa, Humberto

AU - Antelis, Javier M.

AU - Falcón, Luis E.

PY - 2019

Y1 - 2019

N2 - We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network.

AB - We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network.

KW - EEG signals

KW - Motor Imagery

KW - Multi layer perceptron

KW - Neural networks

KW - Power Spectral Density

KW - Spiking Neural Network

KW - Wavelet Decomposition

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DO - 10.1007/978-3-030-21077-9_2

M3 - Contribución a la conferencia

AN - SCOPUS:85068328238

SN - 9783030210762

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 14

EP - 24

BT - Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings

A2 - Carrasco-Ochoa, Jesús Ariel

A2 - Martínez-Trinidad, José Francisco

A2 - Olvera-López, José Arturo

A2 - Salas, Joaquín

PB - Springer Verlag

T2 - 11th Mexican Conference on Pattern Recognition, MCPR 2019

Y2 - 26 June 2019 through 29 June 2019

ER -

Virgilio G CD, Sossa H, Antelis JM, Falcón LE. Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. In Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, Salas J, editors, Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings. Springer Verlag. 2019. p. 14-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-21077-9_2

Motor Imagery Task Classification in EEG Signals with Spiking Neural Network

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Keywords

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