1 code implementation • 21 Nov 2023 • Sung-Yu Chen, Chi-Min Chang, Kuan-Jung Chiang, Chun-Shu Wei
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems.
1 code implementation • 6 Dec 2022 • Xin-Yao Huang, Sung-Yu Chen, Chun-Shu Wei
Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data.
no code implementations • 12 Oct 2022 • Pin-Hua Lai, Bo-Shan Wang, Wei-Chun Yang, Hsiang-Chieh Tsou, Chun-Shu Wei
CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration.
1 code implementation • 5 Oct 2022 • Yue-Ting Pan, Jing-Lun Chou, Chun-Shu Wei
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).
1 code implementation • 10 Jan 2022 • Ya-Lin Huang, Chia-Ying Hsieh, Jian-Xue Huang, Chun-Shu Wei
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding.
no code implementations • 10 Feb 2021 • Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi, Tzyy-Ping Jung
Significance: This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances.
no code implementations • 5 Oct 2018 • Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi, Tzyy-Ping Jung
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication.