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Search : [ author: Kae Won Choi ] (2)

Motor Imagery Decoding with Residual Dense Network

Permana Deny, Sae Won Cheon, Kae Won Choi

http://doi.org/10.5626/JOK.2022.49.5.380

This article proposes a Residual Dense Network (RDN) framework for brain signals during motor imagery (MI) decoding. We designed a decoding framework including feature extraction and a decoding algorithm built on a deep neural network to perform feature learning and decision making. We analyzed the capability of the RDN to decode a public BCI dataset from BCI Competition IV Dataset 2A. Experiments were conducted to evaluate the capability in terms of the performance accuracy for a given dataset and showed that the RDN framework achieved a result of 0.8290, outperforming the previous study using the same dataset benchmark. In conclusion, the RDN provided a decoding framework in a practical brain-computer interface.

Estimation of Finger Motion using Transient EMG Signals

Jin Won Park, Kae Won Choi

http://doi.org/10.5626/JOK.2022.49.2.157

In this paper, we propose a deep learning model for estimating finger movements based on EMG signals. We have also evaluated and analyzed the accuracy of the model. We have applied the U-Net structure, which is widely used in medical image analysis, to our model. In general, U-Net is mainly used for processing of two-dimensional images. However, in this paper, 8-channel one-dimensional time series EMG data is used as inputs, and information about finger movement is obtained as results. We have acquired the data set consisting of 8,000 motions, which is divided into the training and evaluation data sets. The accuracy of the prediction of our model is about 89.32%.


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