Day 1 |
Session 1
(Clinical BCI Applications)
(Chair: Andrea Kubler) |
13:10~13:40 (30') |
|
TU Berlin |
Immediate Brain Plasticity After One Hour of Brain-Computer Interface |
13:40~13:55 (15') |
Vladimir Maksimenko |
Innopolis University |
Brain-Computer Interface for the Epileptic Seizures Prediction and Prevention |
13:55~14:10 (15') |
Sunil Kumar Prabhakar |
Korea University |
Eigen Vector Method with Swarm and Non Swarm Intelligence Techniques for Epileptic Seizure Classification |
14:10~14:25 (15') |
Miseon Shim |
Kumoh National Institute of Technology |
Altered Cortical Activation and Functional Network in Post-Traumatic Stress Disorder (PTSD) during an Auditory Cognitive Processing |
14:25~14:40 (15') |
Junmo Yang |
DGIST |
Probe Configuration Design for Closed-loop Multi-Channel fNIRS-tDCS BCI |
Break (30') |
Session 2
(Deep Learning for BCI)
(Chair: Heung-Il Suk) |
15:10~15:40 (30') |
Alexander Meyer |
Deutsches Herzzentrum Berlin |
Real-time predictive analytics in critical care |
15:40~15:55 (15') |
Jeong-Hyun Cho |
Korea University |
A novel approach to classify natural grasp actions by estimating muscle-activity patterns from EEG signals |
15:55~16:10 (15') |
Hiroto Date |
Niigata University |
Deep Learning for Channel-Agnostic Brain Decoding across Multiple Subjects |
16:10~16:25 (15') |
Wonjun Ko |
Korea University |
VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation |
Break (25') |
Spotlight 1
(Chair: Chang-Hwan Im) |
16:50~17:40 (50') |
BCI2020#14323 |
| BCI2020#14329 |
Implementation of Hardware-based Neural Network using Memristors with Abrupt SET and Gradual RESET Characteristics |
| BCI2020#14332 |
Subject-Independent EEG-based Emotion Recognition using Adversarial Learning |
| BCI2020#14338 |
3D CNN based Multilevel Feature Fusion for Workload Estimation |
| BCI2020#14339 |
Application of Hybrid Brain-Computer Interface with Augmented Reality on Quadcopter Control |
| BCI2020#14342 |
Data Augmentation Effects using Borderline-SMOTE on Classification of a P300-based BCI |
| BCI2020#14344 |
Spatio-Temporal Dynamics of Visual Imagery for Intuitive Brain-Computer Interface |
| BCI2020#14345 |
Classification of Selective Attention based on Steady-State Somatosensory Evoked Potentials using High-Frequency Vibration Stimuli |
| BCI2020#14347 |
Neural Oscillations for Encoding and Decoding Declarative Memory using EEG Signals |
| BCI2020#14350 |
Decoding prefrontal cognitive states from electroencephalography in virtual-reality environment |
| BCI2020#14352 |
Prediction of Visual Memorability with EEG Signals using Deep Neural Networks |
|
|
Day 2 |
Session 3
(Exogenous BCI)
(Chair: Ulman Lindenberger) |
09:00~09:30 (30') |
Andrea Kubler |
University of Wurzburg |
Reducing stimulation intensity in a visual ERP BCI to approach gaze-independent spelling |
| 09:30~09:45 (15') |
Winko An |
Carnegie Mellon University |
Decoding auditory and tactile attention for use in an EEG-based brain-computer interface |
| 09:45~10:00 (15') |
Junhyuk Choi |
University of Science and Technology KIST School |
Robust Semi-synchronous BCI Controller for Brain-Actuated Exoskeleton System |
| 10:00~10:15 (15') |
Maurice Rekrut |
German Research Center for Artificial Intelligence |
Decoding Semantic Categories from EEG Activity in Object-Based Decision Tasks |
| 10:15~10:30 (15') |
Avinash Kumar Singh |
University of Technology Sydney |
Prediction Error Negativity in Physical Human-Robot Collaboration |
Break (30') |
Session 4
(Sensorimotor BCI)
(Chair: Klaus-Robert Muller) |
11:00~11:30 (30') |
Carmen Vidaurre |
TU Berlin |
Brain-computer interface and sensorimotor oscillations: novel perspectives and methods |
| 11:30~11:45 (15') |
Sung-Phil Kim |
Ulsan National Institute of Science and Technology |
Sensory and Motor Information in Primary Motor Cortical Neurons During Arm Reaching To a Moving Target |
| 11:45~12:00 (15') |
Dong Pyo Jang |
Hanyang University |
Deep learning classification features visualization for arm movement brain-computer interface |
| 12:00~12:15 (15') |
Chun Kee Chung |
Seoul National University |
Macroscopic Aspects of Bi-directional BCI in Human |
Lunch & Intermission |
Session 5
(Unobstructive BCI)
(Chair: Alexander Meyer) |
15:30~16:00 (30') |
Ulman Lindenberger |
Max Planck Institute for Human Development |
Human Cognitive Aging: Maintenance Versus Dedifferentiation |
| 16:00~16:15 (15') |
Netiwit Kaongoen |
KAIST |
An Ear-EEG-based Brain-Computer Interface using Concentration Level for Control |
| 16:15~16:30 (15') |
Kyla McMullen |
University of Florida |
The Design of an Algorithmic Modal Music Platform for Eliciting and Detecting Emotion |
| 16:30~16:45 (15') |
Nicholas Opie |
University of Melbourne |
Mechanical Suitability of an Endovascular Brain-Computer Interface |
Break (25') |
Spotlight 2
(Chair: Tae-Eui Kam) |
17:00~17:50 (50') |
BCI2020#14354 |
Filter-bank Local Region Complex-valued Common Spatial Pattern for Motor Imagery Classification |
| BCI2020#14355 |
Transform based Feature Construction Utilizing Magnitude and Phase for Convolutional Neural Network in EEG Signal Classification |
| BCI2020#14360 |
Towards Paradigm-Independent Brain Computer Interfaces |
| BCI2020#14357 |
Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals |
| BCI2020#14358 |
Design and Optimization of a Real-Time Asynchronous BCI Control Strategy for Robotic Manipulator Assistance |
| BCI2020#14366 |
Motor Hotspot Identification Based on Electroencephalography |
| BCI2020#14374 |
Implementation of multi-connected single-channel functional near-infrared spectroscopy system for hyperscanning study |
| BCI2020#14376 |
Importance of Reliable EEG Data in Motor Imagery Classification: Attention Level-based Approach |
| BCI2020#14377 |
Comparative analysis of NIRS-EEG motor imagery data using features from Spatial, Spectral and Temporal domain |
| BCI2020#14383 |
A Convolutional Neural Network Model for Decoding EEG signals in a Hand-Squeeze Task |
| BCI2020#14385 |
Identifying error features in a MI-BCI system using microstates |
|
|
Day 3 |
Pannel Discussion |
10:00~11:30 (90') |
Towards better practical use of BCI in the clinic and daily life
|
| Closing |
