|
|
|
|
|
|
AUTHOR |
AFFILIATION |
TITLE |
| Feb. 21, 2022 (UTC/GMT +09:00) Asia/Seoul) |
Day 1 |
|
13:05~13:10 |
Opening Remark |
Session 1 (Clinical BCI Applications)
(Co-Chairs: Profs. C. Guan & J. S. Kim) |
13:10~13:35
(25') |
C. Guan |
Nanyang University of Technology |
Towards a Holistic Rehabilitation System based on Brain-Computer Interfaces |
13:35~13:55
(20') |
Y.-S. Kweon, G.-H. Shin |
Korea University |
Possibility of Sleep Induction using Auditory Stimulation based on Mental States |
13:55~14:15
(20') |
K. Meng, E. Kim, S. Vogrin, M. J. Cook, F. Goodarzy, D. B. Grayden, C. K. Chung |
The University of Melbourne |
Implementation of a closed-loop BCI system for real-time speech synthesis under clinical constraints |
14:15~14:35 (20') |
C. Piozin, B. Lavrard, C. Simon, J.-Y. Audran, F. Waszak, S. Eskiizmirliler |
Université de Paris |
Motion prediction for the sensorimotor control of hand prostheses with a brain-machine interface using EEG |
14:35~14:55 (20') |
N. B Maimon, L. Molcho, E. Jaul, N. Intrator, J. Barron, O. Meiron |
Tel Aviv University |
EEG reactivity changes captured via mobile BCI device following tDCS intervention - a pilot in disorders of consciousness (DOC) patients |
Break (25') |
Session 2 (Visual Imagery BCI)
(Co-Chairs: Profs. K.-R. Müller & T.-E. Kam) |
15:20~15:45 (25') |
K.-R. Müller |
Technical University of Berlin |
Deep Learning for Whole Brain Cognitive Decoding |
15:45~16:05 (20') |
J. Kilmarx, H. Gamper, D. Emmanouilidou, D. Johnston, E. Cutrell, A. Wilson, I. Tashev |
The University of Texas at Austin |
Investigating Visual Imagery as a BCI Control Strategy: A Pilot Study |
16:05~16:25 (20') |
B.-H. Kwon, J.-H. Cho, B.-H. Lee, J.-H. Jeong |
Korea University |
Decoding Visual Imagery from EEG Signals using Visual Perception Guided Network Training Method |
16:25~16:45 (20') |
H. Ahn, D. Lee |
Korea University |
Decoding 3D Representation of Visual Imagery EEG using Attention-based Dual-Stream Convolutional Neural Network |
Break (20') |
Spotlight 1
(Chair: Prof. T.-E. Kam) |
17:05~17:55 (50') |
4' * 10 papers
(Paper no. 1~10) |
| Feb. 22, 2022 (UTC/GMT +09:00) Asia/Seoul) |
Day 2 |
Session 3 (Efficient BCI)
(Chair: Dr. H. Kim) |
09:00~09:25
(25') |
S. Becker |
McMaster University |
BCI Illiteracy: It's us, not them. Optimizing BCIs for individual brains |
| 09:25~09:45 (20') |
Z. Chen, M. Mousavi, V. de Sa |
University of California San Diego |
Multi-subject unsupervised transfer with weighted subspace alignment for common spatial patterns |
| 09:45~10:05 (20') |
D.-K. Han, S. Musellim, D.-Y. Kim, J.-H. Jeong |
Korea University |
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface |
| 10:05~10:25 (20') |
J.-H. Jeong, K.-T. Kim, S. J. Lee, D.-J. Kim, H. Kim |
KIST |
CNN-based Subject-Transfer Approach for Training Minimized Lower-Limb MI-BCIs |
| 10:25~10:45 (20') |
I. Dolzhikova, B. Abibullaev, A. Zollanvari |
Nazarbayev University |
An Ensemble of Convolutional Neural Networks for Zero-Calibration ERP-Based BCIs |
| Break (25') |
Session 4 (Innovative BCI Applications)
(Chair: Prof. S. W. Lee) |
11:10~11:35 (25') |
K. V. Shenoy |
Stanford University |
Next-generation BCIs: Brain-to-text Communication via Attempted Handwriting |
| 11:35~11:55 (20') |
S. Park, M.-S. Kim, H. Nam, C. -H. Im |
Hanyang University |
Development of an In-Car Environment Control System Using an SSVEP-based BCI with Visual Stimuli Presented on a Head-Up Display |
| 11:55~12:15 (20') |
G.-H. Shin, Y.-S. Kweon |
Korea University |
Differential EEG Characteristics during Working Memory Encoding and Re-encoding |
| 12:15~12:35 (20') |
Y. Shin, J. Kwon, J. S. Kim, C. K. Chung |
Seoul National University Hotspital |
Introduction of Beat Oscillation to Improve the Performance of Music BCI Decoder |
| Lunch (85') |
Session 5 (Advanced Methodolgies for BCI)
(Chair: Prof. D.-J. Kim) |
14:00~14:20 (20') |
T. Fang, W. Mu, Z. Song, S. Le, Y. Zhang, X. Zhang, G. Zhan, J. Wang, L. Zhang, J. Bin, L.g Liu, P. Wang, X. Kang |
Fudan University |
Denoising of EEG Signal Using Permutation Entropy and Source Imaging |
| 14:20~14:40 (20') |
Y. H. Kang, D. Kim, S. W. Lee |
KAIST |
Meta-BCI: Perspectives on a role of self-supervised learning in meta brain computer interface |
| 14:40~15:00 (20') |
D. -H. Shin, D. -H. Ko, J. -W. Han, T. -E. Kam |
Korea University |
Evolutionary Reinforcement Learning for Automated Hyperparameter Optimization in EEG Classification |
| 15:00~15:25 (25') |
V. Nikulin |
Max Planck Institute |
Predicting task performance and brain responses with ongoing neural activity |
| Break (25') |
Session 6 (Deep Learing for BCI)
(Chair: Prof. H. J. Hwang) |
15:50~16:15 (25') |
R. Goebel |
Maastricht University |
Reading Imagined Letter Shapes from the Mind’s Eye using Real-time 7 Tesla fMRI |
| 16:15~16:35 (20') |
J.-S. Bang, S.-W. Lee |
Korea University |
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification |
| 16:35~16:55 (20') |
M. Mametkulov, A. Artykbayev, D. Koishigarina, A. Kenessova, K. Razikhova, T. Kang, C. Wallraven, S. Fazli |
Nazarbayev University |
Explainable machine learning for memory-related decoding via TabNet and non-linear features |
| 16:55~17:15 (20') |
J. Shin, W. Chung |
Korea University |
Motor Imagery Classification based on Multi-Kernel CNN with the amalgamated Cross Entropy Loss |
Spotlight 2
(Chair: Prof. D.-O. Won) |
17:15~18:05 (50') |
4' * 10 papers
(Paper no. 11~20) |
| Feb. 23, 2022 (UTC/GMT +09:00) Asia/Seoul) |
Day 3 |
Session 7 (Silent BCI)
(Chair: Dr. J.-H. Jeong)
|
| 09:00~09:20 (20') |
S.-H. Lee, Y.-E. Lee, S.-W. Lee |
Korea University |
Toward Imagined Speech based Smart Communication System: Potential Applications on Metaverse Conditions |
| 09:20~9:40 (20') |
J. Choi, N. Kaongoen, S. Jo |
KAIST |
Investigation on Effect of Speech Imagery EEG Data Augmentation with Actual Speech |
| 9:40~10:00 (20') |
D.-H. Lee, S.-J. Kim, K.-W. Lee |
Korea University |
Decoding High-level Imagined Speech using Attention-based Deep Neural Networks |
|
10:00~10:05 (05') |
Closing Remarks |
Spotlight List
| # |
Authors |
Title |
| Spotlight 1 |
| 1 |
M. H. Kim, D. Kim, E. Jo, S.-W. Lee |
Goal-Driven Atari Environment |
| 2 |
N. Kaongoen, J. Jeon, S. Jo |
Enhancing the Performance of P300-based BCIs by tDCS of the Left VL-PFC |
| 3 |
B.-H. Lee, B.-H. Kwon, J.-H. Cho |
A Factorization Approach for Motor Imagery Classification |
| 4 |
S. Jeong, W. Ko, A. W. Mulyadi, H.-I. Suk |
Continuous Riemannian Geometric Learning for Sleep Staging Classification |
| 5 |
M.-K. Kim, J.-H. Cho, H.-B. Shin |
Recognition of Tactile-related EEG Signals Generated by Self-touch |
| 6 |
Y. Pyo, S. Nahm, J.-C. Jeong |
Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks |
| 7 |
W. Mu, J. Wang, T. Fang, P. Wang, L. Liu, A. Wang, L. Niu, J. Bin, J. Zhang, J. Jia, L. Zhang, X. Kang |
EEG Channel Selection Methods for Motor Imagery in Brain Computer Interface |
| 8 |
A. Wang, J. Wang, L. Liu, W. Mu, P. Wang, J. Zhang, Z. Song, Y. Zhang, G. Zhan, X. Zhang, L. Zhang, X. Kang |
SEEG signal processing methods in the application of epilepsy recognition |
| 9 |
C. Simon, K. Ruddy |
A wireless, wearable Brain-Computer Interface for neurorehabilitation at home; A feasibility study |
| 10 |
A. E. Voinas, R. Das, M. A. Khan, I. Brunner, S. Puthusserypady |
Motor Imagery EEG Signal Classification for Stroke Survivors Rehabilitation |
| |
| Spotlight 2 |
| 11 |
J.-H. Cho, B.-H. Kwon, B.-H. Lee, S.-W. Lee |
Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals |
| 12 |
H.-T. Lee, S. Jun, J. S. Kim, C. K.e Chung, H.-J. Hwang |
Decoding the Performance of a Memory Task Using Single-trial Intracranial EEG |
| 13 |
M. Shim, S.-H. Lee, H.-J. Hwang |
A novel neurophysiological feature based on quantifying EEG data for seperating patients in psychiatric disorders with comorbidities |
| 14 |
M. Alimardani, D.-E. Gherman |
Individual Differences in Motor Imagery BCIs: a Study of Gender, Mental States and Mu Suppression |
| 15 |
J.-W. Hyung, S. Lee, H. Kim, D.-J. Kim |
Importance of the Quantitative Change of EEG Theta/Beta Ratio Between Preparation and Motor Imagery: Correlation with the Performance of Classification |
| 16 |
H. Kwon, C. E. Hwang, S. Jo |
Vision Combined with MI-Based BCI in Soft Robotic Glove Control |
| 17 |
Y.-E. Lee, S.-H. Lee |
EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech |
| 18 |
D. Heo, M. Kim, J. Kim, Y. J. Choi, S.-P. Kim |
Use of Brain-Computer Interfaces in Different Postures for Daily Living Applications |
| 19 |
G. Zoumpourlis, I. Patras |
CovMix: Covariance Mixing Regularization for Motor Imagery Decoding |
| 20 |
P. Lee, S. Hwang, J. Lee, M. Shin, S. Jeon, H. Byun |
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition |
|
