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The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different principles are applied to BCI depending on the application, because strengths and weaknesses vary according to each BCI method. Therefore, BCI should be able to predict a user state to apply suitable principles to the system. This study measured electroencephalography signals in four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery) from 10 healthy subjects. Mutual information from 64 channels was calculated as brain connectivity. We used a convolutional neural network to predict a user state, where brain connectivity was the network input. We applied five-fold cross-validation to evaluate the proposed method. Mean accuracy for user state classification was 88.25 ± 2.34%. This implies that the system can change the BCI principle using brain connectivity. Thus, a BCI user can control various applications according to their intentions.
Seung-Min Park; Hong-Gi Yeom; Kwee-Bo Sim. User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network. Electronics 2021, 10, 1158 .
AMA StyleSeung-Min Park, Hong-Gi Yeom, Kwee-Bo Sim. User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network. Electronics. 2021; 10 (10):1158.
Chicago/Turabian StyleSeung-Min Park; Hong-Gi Yeom; Kwee-Bo Sim. 2021. "User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network." Electronics 10, no. 10: 1158.
In this paper, we propose an advanced parameter-setting-free (PSF) scheme to solve the problem of setting the parameters for the harmony search (HS) algorithm. The use of the advanced PSF method solves the problems of the conventional PSF scheme that results from a large number of iterations and shows good results compared to fixing the parameters required for the HS algorithm. In addition, unlike the conventional PSF method, the advanced PSF method does not use additional memory. We expect the advanced PSF method to be applicable to various fields that use the HS algorithm because it reduces the memory utilization for operations while obtaining better results than conventional PSF schemes.
Yong-Woon Jeong; Seung-Min Park; Zong Woo Geem; Kwee-Bo Sim. Advanced Parameter-Setting-Free Harmony Search Algorithm. Applied Sciences 2020, 10, 2586 .
AMA StyleYong-Woon Jeong, Seung-Min Park, Zong Woo Geem, Kwee-Bo Sim. Advanced Parameter-Setting-Free Harmony Search Algorithm. Applied Sciences. 2020; 10 (7):2586.
Chicago/Turabian StyleYong-Woon Jeong; Seung-Min Park; Zong Woo Geem; Kwee-Bo Sim. 2020. "Advanced Parameter-Setting-Free Harmony Search Algorithm." Applied Sciences 10, no. 7: 2586.