TY - GEN
T1 - Artificial neural networks and common spatial patterns for the recognition of motor information from EEG signals
AU - Virgilio Gonzalez, Carlos Daniel
AU - Sossa Azuela, Juan Humberto
AU - Antelis, Javier M.
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - This paper proposes the use of two models of neural networks (Multi Layer Perceptron and Dendrite Morphological Neural Network) for the recognition of voluntary movements from electroencephalographic (EEG) signals. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals were recorded from eighteen healthy subjects performing self-paced reaching movements. Three classification scenarios were evaluated in each participant: Relax versus Intention, Relax versus Execution and Intention versus Execution. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical methods called Root Mean Square, Variance, Standard Deviation and Mean. The results showed that the models of neural networks provided decoding accuracies above chance level, whereby, it is able to detect a movement prior its execution. On the basis of these results, the neural networks are a powerful promising classification technique that can be used to enhance performance in the recognition of motor tasks for BCI systems based on electroencephalographic signals.
AB - This paper proposes the use of two models of neural networks (Multi Layer Perceptron and Dendrite Morphological Neural Network) for the recognition of voluntary movements from electroencephalographic (EEG) signals. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals were recorded from eighteen healthy subjects performing self-paced reaching movements. Three classification scenarios were evaluated in each participant: Relax versus Intention, Relax versus Execution and Intention versus Execution. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical methods called Root Mean Square, Variance, Standard Deviation and Mean. The results showed that the models of neural networks provided decoding accuracies above chance level, whereby, it is able to detect a movement prior its execution. On the basis of these results, the neural networks are a powerful promising classification technique that can be used to enhance performance in the recognition of motor tasks for BCI systems based on electroencephalographic signals.
KW - Brain computer interface
KW - Common Spatial Pattern
KW - Dendrite Morphological Neural Network
KW - EEG signals
KW - Motor task
KW - Multilayer Perceptron
UR - http://www.scopus.com/inward/record.url?scp=85059950834&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04491-6_9
DO - 10.1007/978-3-030-04491-6_9
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 - 110
EP - 122
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 -