A5057593158- EEG and Brain-Computer Interfaces
- Gaze Tracking and Assistive Technology
- Emotion and Mood Recognition
- Advanced Neuroimaging Techniques and Applications
- Functional Brain Connectivity Studies
- Advanced Battery Technologies Research
- Tensor decomposition and applications
- Advanced MRI Techniques and Applications
- Stroke Rehabilitation and Recovery
- Sleep and Work-Related Fatigue
- Shoulder Injury and Treatment
- Tribology and Wear Analysis
- Total Knee Arthroplasty Outcomes
- Blind Source Separation Techniques
University of Houston
2021-2023
Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on show resource utilization because they use fully-supervised methods. Therefore, this study, we applied self-supervised methods improve efficiency resources usage. We employed a approach train deep multi-task...
The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed generate high-quality artificial samples. First, EEG for different emotions are preprocessed as differential entropy under five frequency bands and divided into segments with 5s...
Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the process, their effects limited since patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial investigate of BCI-controlled pedaling system (BCI-PT) on motor and...
Protocols have been proposed to optimize neuromodulation targets and parameters increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal simultaneously via exploring test–retest reliability protocols. In this study, we employed a publicly available structural resting-state functional magnetic resonance imaging (fMRI) dataset investigate inferred from our customized protocol examine over scanning time. 57 healthy...