We.1.2.4
MTSSRL-MD: Multi-Task Self-Supervised Representation Learning for EEG Signals across Multiple Datasets
I-Hui Li, Oscar Tai-Yuan Chen, Vincent S. Tseng, National Yang Ming Chiao Tung University
Session:
We.1.2: Generative and Self-Supervised Learning for Neural Signals Oral
Track:
Biomedical Signal Processing
Location:
Room 801A
Presentation Time:
Wednesday, 29 July, 09:15 - 09:30
Session Co-Chairs:
Bramsh Qamar Chandio and Xinqi Bao
Presentation
Discussion
Resources
No resources available.
Session We.1.2
We.1.2.1: Diffusion-Based Heart Sound Generation Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening
Xinqi Bao, KTH Royal Institute of Technology; Jia Bi, Rutherford Appleton Laboratory; Xin Chen, Peng Cheng Laboratory; Ernest Kamavuako,, King's College London; Saikat Chatterjee, KTH Royal Institute of Technology
We.1.2.2: Random Feature Mapping vs. Last-Layer Fine-Tuning for Biosignal Transfer Learning: Toward a Zero-Source Baseline
Takayuki Hoshino, Keio University / National Institute of Advanced Industrial Science and Technology; Suguru Kanoga, National Institute of Advanced Industrial Science and Technology; Atsushi Aoyama, Keio University
We.1.2.3: RL-BIOAUG: LABEL-EFFICIENT REINFORCEMENT LEARNING FOR SELF-SUPERVISED EEG REPRESENTATION LEARNING
Cheol-Hui Lee, Hwa-Yeon Lee, Seoul National University Hospital; Dong-Joo Kim, Korea Unversity
We.1.2.4: MTSSRL-MD: Multi-Task Self-Supervised Representation Learning for EEG Signals across Multiple Datasets
I-Hui Li, Oscar Tai-Yuan Chen, Vincent S. Tseng, National Yang Ming Chiao Tung University
We.1.2.5: WaveTok: A Wavelet Tokenizer for Self-Supervised EEG Representation Learning
Jing Wang, Ercan Engin Kuruoglu, Tsinghua University
We.1.2.6: Class-Conditioned Diffusion-Based EEG Generation for Motor Imagery in the Spectral Domain
Deeksha Kashyap, Srirama Swamy, Chaitanya Makkar, Gowri Srinivasa, PES University
Resources
No resources available.