TY - GEN
T1 - Classification of Imagined Speech Using Siamese Neural Network
AU - Lee, Dong Yeon
AU - Lee, Minji
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported in part by the Institute for Information & Communications Technology Promotion (IITP) grant, funded by the Korea government (MSIT) (No. 2015-0-00185, Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain Computer Interface; No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning; No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University))
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Imagined speech is spotlighted as a new trend in the brain-machine interface due to its application as an intuitive communication tool. However, previous studies have shown low classification performance, therefore its use in real-life is not feasible. In addition, no suitable method to analyze it has been found. Recently, deep learning algorithms have been applied to this paradigm. However, due to the small amount of data, the increase in classification performance is limited. To tackle these issues, in this study, we proposed an end-to-end framework using Siamese neural network encoder, which learns the discriminant features by considering the distance between classes. The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda (left), adelante (forward), and atrás (backward)) were classified using the raw electroencephalography (EEG) signals. We obtained a 6-class classification accuracy of 31.40 ± 2.73% for imagined speech, which significantly outperformed other methods. This was possible because the Siamese neural network, which increases the distance between dissimilar samples while decreasing the distance between similar samples, was used. In this regard, our method can learn discriminant features from a small dataset. The proposed framework would help to increase the classification performance of imagined speech for a small amount of data and implement an intuitive communication system.
AB - Imagined speech is spotlighted as a new trend in the brain-machine interface due to its application as an intuitive communication tool. However, previous studies have shown low classification performance, therefore its use in real-life is not feasible. In addition, no suitable method to analyze it has been found. Recently, deep learning algorithms have been applied to this paradigm. However, due to the small amount of data, the increase in classification performance is limited. To tackle these issues, in this study, we proposed an end-to-end framework using Siamese neural network encoder, which learns the discriminant features by considering the distance between classes. The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda (left), adelante (forward), and atrás (backward)) were classified using the raw electroencephalography (EEG) signals. We obtained a 6-class classification accuracy of 31.40 ± 2.73% for imagined speech, which significantly outperformed other methods. This was possible because the Siamese neural network, which increases the distance between dissimilar samples while decreasing the distance between similar samples, was used. In this regard, our method can learn discriminant features from a small dataset. The proposed framework would help to increase the classification performance of imagined speech for a small amount of data and implement an intuitive communication system.
KW - Siamese neural network
KW - brain-machine interface
KW - deep learning
KW - end-to-end framework
KW - imagined speech
UR - http://www.scopus.com/inward/record.url?scp=85098887152&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9282982
DO - 10.1109/SMC42975.2020.9282982
M3 - Conference contribution
AN - SCOPUS:85098887152
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2979
EP - 2984
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Y2 - 11 October 2020 through 14 October 2020
ER -