190 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Theerawit Wilaiprasitporn2 profiles24 · h 22
- Phairot Autthasan8 · h 8
- Nannapas Banluesombatkul6 · h 7
- Pitshaporn Leelaarporn6 · h 13
- P. Manoonpong6 · h 32
- Apiwat Ditthapron5 · h 6
- E. Chuangsuwanich5 · h 20
- Nat Dilokthanakul2 profiles5 · h 10
- Thapanun Sudhawiyangkul5 · h 13
- Narin Kunaseth4 · h 5
- Payongkit Lakhan4 · h 5
- Rattanaphon Chaisaen4 · h 7
- Chulalongkorn UniversityTH9 papers
- Kasetsart UniversityTH8 papers
- King Mongkut's University of Technology ThonburiTH6 papers
- Mahidol UniversityTH5 papers
- University of Southern DenmarkDK4 papers
- Worcester Polytechnic InstituteUS4 papers
- Christian-Albrechts-Universität zu KielDE2 papers
- King Chulalongkorn Memorial HospitalTH2 papers
- Korea Advanced Institute of Science and TechnologyKR2 papers
- KU LeuvenBE2 papers
- Macquarie UniversityAU2 papers
- Maersk (Denmark)DK2 papers
7 papers · 2 filters
Consumer Grade Brain Sensing for Emotion Recognition
Payongkit Lakhan, Nannapas Banluesombatkul, Vongsagon Changniam +5
For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities.…
A Single-Channel Consumer-Grade EEG Device for Brain-Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation
Phairot Autthasan, Xiangqian Du, Jetsada Arnin +6
Brain-Computer interfaces (BCIs) play a significant role in easing neuromuscular patients on controlling computers and prosthetics. Due to their high signal-to-noise ratio, steady-…
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning
Patcharin Cheng, Phairot Autthasan, Boriwat Pijarana +2
In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The proposed DL approach i…
Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification
Payongkit Lakhan, Apiwat Ditthapron, Nannapas Banluesombatkul +1
Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classi…
Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
Nannapas Banluesombatkul, Thanawin Rakthanmanon, Theerawit Wilaiprasitporn
Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnogr…
Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder
Apiwat Ditthapron, Nannapas Banluesombatkul, Sombat Ketrat +2
The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the…
Affective EEG-Based Person Identification Using the Deep Learning Approach
Theerawit Wilaiprasitporn, Apiwat Ditthapron, Karis Matchaparn +3
Electroencephalography (EEG) is another mode for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is per…