A5086075497- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Photonic and Optical Devices
- Semiconductor Lasers and Optical Devices
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Gaze Tracking and Assistive Technology
- Muscle activation and electromyography studies
- Optical Network Technologies
- Advanced Fiber Laser Technologies
- Blind Source Separation Techniques
- Advanced Photonic Communication Systems
- Visual perception and processing mechanisms
- Tactile and Sensory Interactions
- Neural and Behavioral Psychology Studies
- Neurobiology and Insect Physiology Research
- Heat Transfer and Optimization
- Circadian rhythm and melatonin
- Advanced Sensor and Energy Harvesting Materials
- Functional Brain Connectivity Studies
- Photoreceptor and optogenetics research
- Retinal Development and Disorders
- Refrigeration and Air Conditioning Technologies
- Heat Transfer and Boiling Studies
- Neuroendocrine regulation and behavior
University of Science and Technology of China
2020-2025
Shanghai Jiao Tong University
2012-2025
Lanzhou Jiaotong University
2005-2024
Zhejiang University
2012-2023
Hisense (China)
2015-2023
University of Minnesota
2016-2020
Carnegie Mellon University
2018-2020
Hefei National Center for Physical Sciences at Nanoscale
2020
Dongfeng Motor Group (China)
2019
State Key Laboratory of Medical Neurobiology
2016-2018
Abstract Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI control virtual objects, such as computer cursors helicopters, real-world wheelchairs quadcopters, has demonstrated promise of technologies. However, controlling robotic arm complete reach-and-grasp tasks efficiently yet be shown. In this study, we found that group 13 subjects could willingly modulate activity with high accuracy for...
Noninvasive neuroimaging and increased user engagement improve EEG-based neural decoding facilitate real-time 2D robotic device control.
A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., classification left/ stimulation right/ imagery. In paradigm, wearable vibrotactile rings are used stimulate both skin on wrists. Subjects required perform tasks according randomly presented cues (i.e., hand imagery, or sensation). Two-way ANOVA statistical analysis showed a significant group...
Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied wearable scenarios. This research presented a compact device that could integrated into socket. The miniaturized system included four A-mode transducers for sensing musculature deformations, signal excitation/acquisition module, control module. size of the 65*75*25 mm, weighing only 85 g. For first time,...
Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance human-machine interfaces such as gesture recognition and proportional control. However, neural decoding across multiple tasks real-time remains challenging, which limits its wide application. In this work, we proposed a hand method by unit (MU) discharges ( 10) motion-wise way.The EMG signals were first divided into...
Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control robotic arm. Currently, BCI of arm complete the reaching grasping tasks in an unstructured environment is still challenging because current does not meet requirement manipulating multi-degree accurately robustly. based on steady-state visual evoked potential (SSVEP) could output high information transfer rate; however, conventional SSVEP paradigm failed move continuously users have...
High performance of the brain-computer interface (BCI) needs efficient algorithms to extract discriminative features from raw electroencephalography (EEG) signals. In this paper, we present a novel scheme spatial spectral for motor imagery-based BCI. The learning task is formulated by maximizing mutual information between (MMISS) and class labels, which unique objective function directly related Bayes classification error optimized. are assumed follow parametric Gaussian distribution, has...
We report simple and compact V-cavity semiconductor laser capable of full-band wavelength tuning. A half-wave coupler is used to obtain high side-mode suppression ratio (SMSR) without any grating or epitaxial regrowth. Temperature induced gain spectrum shift employed in combination with the Vernier tuning mechanism extend range beyond free spectral limit. Wavelength 50 channels at 100GHz spacing SMSR up 38 dB has been demonstrated. show that a temperature variation 35°C, can be extended by...
Objective: While noninvasive electroenceph-alography (EEG) based brain-computer interfacing (BCI) has been successfully demonstrated in two-dimensional (2-D) control tasks, little work published regarding its extension to practical three-dimensional (3-D) control. Methods: In this study, we developed a new BCI approach for 3-D by combining novel form of endogenous visuospatial attentional modulation, defined as overt spatial attention (OSA), and motor imagery (MI). Results: OSA modulation...
Electroencephalography based brain-computer interfaces (BCIs) show promise of providing an alternative communication channel between the brain and external device. It is well acknowledged that BCI control a skill could be improved through practice training. In this study, we explore change behavioral performance as electrophysiological properties across three training sessions in pool 42 human subjects. Our results group average accuracy information transfer rate significantly third session...
Objective: Brain-computer interface (BCI) decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR) based BCI performance by integrating motor tasks with tactile stimulation. Methods: Eleven patients were recruited for three experimental conditions, i.e., attempt (MA) condition, stimulation (TS) and stimulation-assisted...
Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on signals from sensorimotor cortex, leaving subcortical regions other cortical related to movements largely unexplored. As an intracranial technique presurgical assessments...