TY - GEN
T1 - Recognizing motor imagery tasks using deep multi-layer perceptrons
AU - Arce, Fernando
AU - Zamora, Erik
AU - Hernández, Gerardo
AU - Antelis, Javier M.
AU - Sossa, Humberto
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - A brain-computer interface provides individuals with a way to control a computer. However, most of these interfaces remain mostly utilized in research laboratories due to the absence of certainty and accuracy in the proposed systems. In this work, we acquired our own dataset from seven able-bodied subjects and used Deep Multi-Layer Perceptrons to classify motor imagery encephalography signals into binary (Rest vs Imagined and Left vs Right) and ternary classes (Rest vs Left vs Right). These Deep Multi-Layer Perceptrons were fed with power spectral features computed with the Welch’s averaged modified periodogram method. The proposed architectures outperformed the accuracy achieved by the state-of-the-art for classifying motor imagery bioelectrical brain signals obtaining 88.03%, 85.92% and 79.82%, respectively, and an enhancement of 11.68% on average over the commonly used Support Vector Machines.
AB - A brain-computer interface provides individuals with a way to control a computer. However, most of these interfaces remain mostly utilized in research laboratories due to the absence of certainty and accuracy in the proposed systems. In this work, we acquired our own dataset from seven able-bodied subjects and used Deep Multi-Layer Perceptrons to classify motor imagery encephalography signals into binary (Rest vs Imagined and Left vs Right) and ternary classes (Rest vs Left vs Right). These Deep Multi-Layer Perceptrons were fed with power spectral features computed with the Welch’s averaged modified periodogram method. The proposed architectures outperformed the accuracy achieved by the state-of-the-art for classifying motor imagery bioelectrical brain signals obtaining 88.03%, 85.92% and 79.82%, respectively, and an enhancement of 11.68% on average over the commonly used Support Vector Machines.
KW - Brain computer interface
KW - Deep multi-layer perceptrons
KW - Electroencephalography
KW - Motor imagery
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85050472983&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-96133-0_35
DO - 10.1007/978-3-319-96133-0_35
M3 - Contribución a la conferencia
SN - 9783319961323
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 468
EP - 482
BT - Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
A2 - Perner, Petra
PB - Springer Verlag
T2 - 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Y2 - 15 July 2018 through 19 July 2018
ER -