A5009497783- EEG and Brain-Computer Interfaces
- Emotion and Mood Recognition
- Sleep and Wakefulness Research
- Non-Invasive Vital Sign Monitoring
- Obstructive Sleep Apnea Research
- Indoor and Outdoor Localization Technologies
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Advanced Memory and Neural Computing
- User Authentication and Security Systems
- Wireless Networks and Protocols
- Ultra-Wideband Communications Technology
- Context-Aware Activity Recognition Systems
- Neuroscience of respiration and sleep
- Cognitive Computing and Networks
- Sleep and related disorders
- Brain Tumor Detection and Classification
Vidyasirimedhi Institute of Science and Technology
2018-2024
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 automatic sleep stage classification conundrum. However, difficulties can be posed replacing clinicians with system due differences many aspects found individual bio-signals, causing inconsistency performance model on every incoming individual. Thus, we aim explore feasibility using a novel approach, capable...
For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring distinctive brain activities. An array datasets been generated with use diverse emotion-eliciting stimuli and resulting brainwave responses conventionally captured high-end EEG devices. However, applicability these devices is to some extent limited by practical constraints may prove difficult be deployed highly mobile context omnipresent everyday...
Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages using DL for sleep Apnea-Hypopnea severity classification. To reduce complexity clinical diagnosis Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed scheme into one single Airflow (AF) signal (subset PSG). Seventeen features have been extracted from AF then fed Neural Networks classify two studies. First, a binary...
Recognizing movements during sleep is crucial for the monitoring of patients with disorders, and utilization ultra-wideband (UWB) radar classification human postures has not been explored widely. This study investigates performance an off-the-shelf single antenna UWB in a novel application postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, time series data augmentation aims to classify four standard SPTs. SPN exhibits ability capture both...
Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well modern day deep methods applied with promising results. In this paper we present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) graph (GCNN). The benefit of the CNN in automatic feature extraction and capability GCNN connectivity between EEG electrodes through representation are jointly exploited. We...