TY - GEN
T1 - Classification of motor imagery EEG signals with CSP filtering through neural networks models
AU - Virgilio Gonzalez, Carlos Daniel
AU - Sossa Azuela, Juan Humberto
AU - Rubio Espino, Elsa
AU - Ponce Ponce, Victor H.
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements; in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks; Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks; Subject 1 - 93.9% ± 3.9% and Subject 2 - 68.7% ± 7%.
AB - The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements; in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks; Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks; Subject 1 - 93.9% ± 3.9% and Subject 2 - 68.7% ± 7%.
KW - Common spatial pattern
KW - Dendrite Morphological Neural Network
KW - EEG signals
KW - Motor imagery
KW - Multilayer Perceptron
KW - One vs Rest
KW - Pair-Wise
KW - RMS
UR - http://www.scopus.com/inward/record.url?scp=85059959983&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04491-6_10
DO - 10.1007/978-3-030-04491-6_10
M3 - Contribución a la conferencia
SN - 9783030044909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 123
EP - 135
BT - Advances in Soft Computing - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, Proceedings
A2 - de Lourdes Martínez-Villaseñor, María
A2 - Batyrshin, Ildar
A2 - Ponce Espinosa, Hiram Eredín
PB - Springer Verlag
T2 - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
Y2 - 22 October 2018 through 27 October 2018
ER -