no code implementations • 17 Aug 2022 • Payongkit Lakhan, Nannapas Banluesombatkul, Natchaya Sricom, Korn Surapat, Ratha Rotruchiphong, Phattarapong Sawangjai, Tohru Yagi, Tulaya Limpiti, Theerawit Wilaiprasitporn
The benefit of the CNN in automatic feature extraction and the capability of GCNN in learning connectivity between EEG electrodes through graph representation are jointly exploited.
1 code implementation • 8 Apr 2020 • Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn, Payongkit Lakhan, Busarakum Chaitusaney, Nattapong Jaimchariyatam, Ekapol Chuangsuwanich, Wei Chen, Huy Phan, Nat Dilokthanakul, Theerawit Wilaiprasitporn
This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
no code implementations • 5 Jul 2018 • Theerawit Wilaiprasitporn, Apiwat Ditthapron, Karis Matchaparn, Tanaboon Tongbuasirilai, Nannapas Banluesombatkul, Ekapol Chuangsuwanich
\textcolor{red}{We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)}.