A5006051351- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- ECG Monitoring and Analysis
- Muscle activation and electromyography studies
- Neural Networks and Applications
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Gaze Tracking and Assistive Technology
- Hand Gesture Recognition Systems
- Wireless Communication Security Techniques
- Advanced Wireless Communication Technologies
- Emotion and Mood Recognition
- Antenna Design and Analysis
- Advanced Algorithms and Applications
- Advanced Sensor and Control Systems
- Energy Harvesting in Wireless Networks
- Medical Image Segmentation Techniques
- Advanced MIMO Systems Optimization
- Retinal Imaging and Analysis
- Piezoelectric Actuators and Control
- Advanced Memory and Neural Computing
- Face and Expression Recognition
- Antenna Design and Optimization
- Video Surveillance and Tracking Methods
- Machine Learning in Bioinformatics
Jilin Medical University
2004-2024
Jilin University
2015-2024
Beijing University of Posts and Telecommunications
2015-2017
Ministry of Education of the People's Republic of China
2010
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is great importance and significance for treatment epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), employed in presented study. LMD able to decompose an arbitrary signal into series product functions (PFs). Primarily, raw EEG decomposed several PFs, then temporal statistical non-linear features first...
Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous methods have overlooked fusion multi-domain emotion-specific information to improve performance, and faced challenge insufficient interpretability. In this paper, we proposed novel EEG model that combined asymmetry brain hemisphere, spatial, spectral, temporal properties signals, aiming performance. Based on 10-20 standard system, global spatial projection...
With the rapid development in technology, computer aided detection or diagnosis has become an indispensable part of medical industry. Automatic epileptic events is one important subjects that have aroused wide interest from more and investigators. This paper proposes a new model classification multi-category electroencephalogram (EEG) signals using time-frequency image block texture features. The one-dimensional EEG first mapped to domain by means short-time Fourier transform (STFT), which...