BIO-6.6
LEARNING SUBJECT-INVARIANT REPRESENTATIONS FROM SPEECH-EVOKED EEG USING VARIATIONAL AUTOENCODERS
Lies Bollens, Tom Francart, Hugo Van hamme, Katholieke Universiteit Leuven, Belgium
Session:
Machine Learning and Signal Processing for EEG
Track:
Biomedical Imaging and Signal Processing
Location:
Gather Area N
Presentation Time:
Tue, 10 May, 23:00 - 23:45 China Time (UTC +8)
Tue, 10 May, 15:00 - 15:45 UTC
Tue, 10 May, 15:00 - 15:45 UTC
Session Chair:
Selin Aviente, Michigan State University
Session BIO-6
BIO-6.1: COMPOSING GRAPHICAL MODELS WITH GENERATIVE ADVERSARIAL NETWORKS FOR EEG SIGNAL MODELING
Khuong Vo, Manoj Vishwanath, Ramesh Srinivasan, Nikil Dutt, Hung Cao, University of California, Irvine, United States of America
BIO-6.2: DOMAIN-INVARIANT REPRESENTATION LEARNING FROM EEG WITH PRIVATE ENCODERS
David Bethge, Porsche, LMU, Germany; Philipp Hallgarten, Porsche, KIT, Germany; Tobias Grosse-Puppendahl, Mohamed Kari, Porsche, Germany; Ralf Mikut, KIT, Germany; Albrecht Schmidt, LMU, Germany; Ozan Özdenizci, TU Graz, Austria
BIO-6.3: HOLISTIC SEMI-SUPERVISED APPROACHES FOR EEG REPRESENTATION LEARNING
Guangyi Zhang, Ali Etemad, Queen's University, Canada
BIO-6.4: Music Identification Using brain responses to Initial Snippets
Pankaj Pandey, Krishna. P. Miyapuram, IIT Gandhinagar, India; Gulshan Sharma, IIT Ropar, India; Ramanathan Subramanian, University of Canberra, Australia; Derek Lomas, TU Delft, Netherlands
BIO-6.5: MULTI-LEVEL SPATIAL-TEMPORAL ADAPTATION NETWORK FOR MOTOR IMAGERY CLASSIFICATION
Wei Xu, Jing Wang, Ziyu Jia, Zhiqing Hong, Yunze Li, Youfang Lin, Beijing Jiaotong University, China
BIO-6.6: LEARNING SUBJECT-INVARIANT REPRESENTATIONS FROM SPEECH-EVOKED EEG USING VARIATIONAL AUTOENCODERS
Lies Bollens, Tom Francart, Hugo Van hamme, Katholieke Universiteit Leuven, Belgium