105 citations · 310 across the 5 of their papers we have counts for
5 papers
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity
Payongkit Lakhan, Nannapas Banluesombatkul, Natchaya Sricom +6
Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep l…
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Maytus Piriyajitakonkij, Patchanon Warin, Payongkit Lakhan +7
Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human…
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn +8
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the…
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.…
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…