A5038147153- EEG and Brain-Computer Interfaces
- Sleep and Work-Related Fatigue
- Gaze Tracking and Assistive Technology
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Epilepsy research and treatment
- Neuroscience and Neural Engineering
- Advanced Sensor and Control Systems
- Sleep and Wakefulness Research
Beijing University of Posts and Telecommunications
2020-2023
Pandemic-related sleep disorders affect human physical and mental health. The artificial intelligence (AI) based staging with multimodal electrophysiological signals help people diagnose treat disorders. However, the existing AI-based methods could not capture more discriminative modalities adaptively correlate these features. This paper introduces a attention network (MMASleepNet) to efficiently extract, perceive fuse features of signals. MMASleepNet has multi-branch feature extraction...
Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG systems' hardware software are usually closed-source. Its inability to flexible expansion secondary development a major obstacle real-time BCI research. This paper presents the Beijing University of Posts Telecommunications Acquisition Tool System named BEATS. It implements comprehensive system from software, composed analog front end,...
Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect drowsiness, name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on each channel separately identify making them vulnerable variability across different sessions subjects without sufficient data. In this paper, we propose method called Tensor Network Features (TNF)...
High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research quantitative measurement and separation such is still lacking. In this paper, pattern clustering (SPC-HFA) developed. The...
Accurate and reliable detecting of driving fatigue using Electroencephalography (EEG) signals is a method to reduce traffic accidents. So far, it natural cut the part operating steering wheel data away for achieving relatively high accuracy in EEG data. However, segment during also contains valuable information. Moreover, common practice actual driving. In this study, we utilize fatigue. The feature used spectral band power calculates from For each experiment experimental participant,...