A5103015825- Muscle activation and electromyography studies
- Hand Gesture Recognition Systems
- Sensor Technology and Measurement Systems
- Advanced Measurement and Detection Methods
- EEG and Brain-Computer Interfaces
- Advanced Sensor and Energy Harvesting Materials
- Tactile and Sensory Interactions
- Visual perception and processing mechanisms
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Advanced Sensor Technologies Research
- Ergonomics and Musculoskeletal Disorders
Xi'an Jiaotong University
2024-2025
Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a (EMG)-based scheme lower to enhance in Addressing the loss of phase information existing methods, approach combines S-transform energy concentration and multi-channel fusion analysis. EMG signals from six muscles 10 subjects performing four movements (level walk, stair ascent, descent, obstacle crossing) were analyzed. Correlation analysis...
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), improving the recognition performance for new subjects without calibration data is key challenge practical application. Unsupervised transfer learning an effective way to overcome it. However, existing studies focus solely on what transfer, rather than how effectively resulting in unsatisfactory effectiveness or even negative transfer. this study, innovative unsupervised cross-subject method SSVEP-BCI was...
With the development of brain-computer interface industry, large amounts related applications have entered people's vision. BCI based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and high information transmission rates. However, in actual use SSVEP stimulus paradigm, subjects will produce fatigue with use, affect efficiency. In this experiment, an experimental environment consisting two paradigm frequencies (7.5 Hz, 15 Hz), three...
<title>Abstract</title> Lower limb motion recognition of based surface electromyography (EMG) aims to provide a more natural and effective human-computer interaction for intelligent prostheses. Accurate relies on high-quality EMG decoding, the key improving efficiency pattern is optimize signal feature extraction. The phase information cannot be neglected recognition. Therefore, we proposed decoding scheme signals S-transform energy concentration lower recognition, including level walk,...
With the development of modern technology, many people work for a long time around various artificial light sources and electronic equipment, causing them to feel discomfort in their eyes even eye diseases. The industry currently lacks an objective quantitative environmental–visual comfort index that combines subjective indicators. For this experiment, movement electroencephalogram (EEG) signals were collected combination with questionnaire survey preference inquiry comprehensive data...