Publications by authors named "Kyung-Hwan Shim"
Article Synopsis
- Developed a large intuitive dataset for 11 upper extremity movement tasks using various neurophysiological signals from 25 participants, addressing the need for more extensive data in brain-computer interface (BCI) research.
- Validated the dataset through neurophysiological analysis, showing distinct sensorimotor responses and confirming consistency with machine learning classification methods.
- The study provides valuable resources for enhancing BCI decoding performance, facilitating comparisons between real movements and motor imagery, and analyzing session variability, aiming to advance BCI technology.
Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.
IEEE Trans Neural Syst Rehabil Eng
May 2020
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery.
View Article and Find Full Text PDFTrajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System.
Annu Int Conf IEEE Eng Med Biol Soc
July 2019
Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is a useful tool for controlling external devices or supporting a neuro- rehabilitation. In this study, we present the possibility of brain-controlled robot arm system using arm trajectory decoding. To do that, we first constructed the experimental system that can acquire the EEG data for not only movement execution (ME) task but also movement imagery (MI) tasks.
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