A5100375828- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- ECG Monitoring and Analysis
- Neural dynamics and brain function
- Gaze Tracking and Assistive Technology
- Neuroscience and Neural Engineering
- Neural Networks and Applications
- Remote-Sensing Image Classification
- Nonlinear Optical Materials Research
- Photochemistry and Electron Transfer Studies
- Video Surveillance and Tracking Methods
- Speech and Audio Processing
- Porphyrin and Phthalocyanine Chemistry
- Advanced Adaptive Filtering Techniques
- Face and Expression Recognition
- Advanced Chemical Sensor Technologies
- Human Pose and Action Recognition
- Anomaly Detection Techniques and Applications
- Sparse and Compressive Sensing Techniques
- Brain Tumor Detection and Classification
- Functional Brain Connectivity Studies
Jilin University
2018-2023
Jilin Medical University
2019-2023
Jilin Agricultural University
2021-2023
State Council of the People's Republic of China
2019
Jiangnan University
2017
Nanjing University of Science and Technology
2016
Shanghai University
2016
Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability heavily dependent on the performance of feature extraction procedure. In this paper, a method based generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for EEG signals. The influence different distance calculation methods, embedding dimensions tolerances were studied find best...
Electrides, a novel kind of ionic compound in which electrons serve as anions, have been proposed potential second-order nonlinear optical (NLO) materials. In this work, the substituent effects on electride characteristics and NLO behaviour Li@calix[4]pyrrole with an electride-like structure were studied theoretically. The results show that electron-donating electron-withdrawing groups can effectively increase decrease first hyperpolarizability (β 0), respectively, without affecting...
In this paper, we consider the single-channel speech enhancement problem, in which a clean signal needs to be estimated from noisy observation. To capture characteristics of both noise and signals, combine well-known Short-Time-Spectrum-Amplitude (STSA) estimator with machine learning based technique called Multi-frame Sparse Dictionary Learning (MSDL). The former utilizes statistical information for denoising, while latter helps better preserve speech, especially its temporal structure....