A5020896442- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Gaze Tracking and Assistive Technology
- Neural dynamics and brain function
- Blind Source Separation Techniques
- Machine Learning and ELM
- Emotion and Mood Recognition
- Optical Coherence Tomography Applications
Zhengzhou University
2020-2024
Abstract Objective . The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP–BCIs use a computer screen (CS) to present flickering stimuli, which limits flexible actual scenes. Augmented reality (AR) technology provides the ability superimpose stimuli on real world, it considerably expands application scenarios SSVEP–BCI. However, whether...
Currently, most models rarely consider the negative transfer problem in research field of cross-subject EEG emotion recognition. To solve this problem, paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists three modules: 1) GFK common feature extractor: projects source and subjects to Grassmann manifold space, obtains latent features two...
Steady-state visual evoked potentials-based brain-computer interfaces (SSVEP-BCI) has the advantage of high information transfer rate (ITR) and little user training, it a application value in field disability assistance human-computer interaction. Generally SSVEP-BCI requires personal computer screen (PC) to display several repetitive stimuli for inducing SSVEP response, which reduces its portability flexibility. Using augmented reality (AR) glasses worn on head could solve above drawbacks,...
Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside laboratory due its portability and mobility, but performance in real-world scenarios has not been fully studied. In current study, we first investigated effect ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions simulate typical real scenes, while same steady-state visual evoked potentials (SSVEP) stimulus was...
Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single MI tasks. In this work, we conducted recognition imagery EEG signals right proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning features raw as well two-dimensional time-frequency maps at same time. The dataset...