1 citations · 1 across the 1 of their papers we have counts for
7 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…
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…
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…