TY  - JOUR
T1  - Motor Imagery Decoding with Residual Dense Network
AU  - Deny, Permana 
AU  - Cheon, Sae Won 
AU  - Choi, Kae Won 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.5.380
KW  - brain-computer interface
KW  - motor imagery
KW  - BCI Competition IV
KW  - label smoothing
AB  - 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.