What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Telecommunications
Ning Jiang mainly focuses on Electromyography, Artificial intelligence, Proportional myoelectric control, Physical medicine and rehabilitation and Electroencephalography.
His research investigates the connection with Electromyography and areas like Upper limb prosthesis which intersect with concerns in Classifier.
The various areas that Ning Jiang examines in his Artificial intelligence study include Focus, Computer vision and Pattern recognition.
His studies in Pattern recognition integrate themes in fields like Speech recognition and Linear model.
His Proportional myoelectric control research integrates issues from Physical therapy, Wrist and Linear regression.
His research integrates issues of Neuroplasticity and Matched filter in his study of Electroencephalography.
His most cited work include:
- The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (450 citations)
- Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal (306 citations)
- Myoelectric Control of Artificial Limbs—Is There a Need to Change Focus? [In the Spotlight] (241 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Brain–computer interface, Artificial intelligence, Chaotic, Semiconductor laser theory and Electroencephalography.
His work carried out in the field of Brain–computer interface brings together such families of science as Speech recognition and Physical medicine and rehabilitation.
His studies deal with areas such as Signal, Computer vision and Pattern recognition as well as Artificial intelligence.
The study incorporates disciplines such as Proportional control, Artificial neural network and Word error rate in addition to Pattern recognition.
He has included themes like Phase modulation, Synchronization, Electronic engineering, Optical communication and Encryption in his Chaotic study.
His biological study spans a wide range of topics, including Bandwidth and Topology.
He most often published in these fields:
- Brain–computer interface (23.15%)
- Artificial intelligence (20.13%)
- Chaotic (18.46%)
What were the highlights of his more recent work (between 2017-2021)?
- Brain–computer interface (23.15%)
- Electroencephalography (16.11%)
- Artificial intelligence (20.13%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Brain–computer interface, Electroencephalography, Artificial intelligence, Pattern recognition and Semiconductor laser theory.
His Brain–computer interface study contributes to a more complete understanding of Neuroscience.
His Electroencephalography study combines topics in areas such as Neurorehabilitation, Audiology, Stimulus, Speech recognition and Visualization.
His Artificial intelligence study integrates concerns from other disciplines, such as Signal and Computer vision.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Proportional myoelectric control and Word error rate.
His Semiconductor laser theory study incorporates themes from Phase modulation, Random number generation and Wideband.
Between 2017 and 2021, his most popular works were:
- Online mapping of EMG signals into kinematics by autoencoding (35 citations)
- Chaos synchronization and communication in closed-loop semiconductor lasers subject to common chaotic phase-modulated feedback. (27 citations)
- Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization. (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Telecommunications
Ning Jiang mostly deals with Electroencephalography, Brain–computer interface, Artificial intelligence, Pattern recognition and Audiology.
His research in Electroencephalography intersects with topics in Rehabilitation, Lateralization of brain function and Physical medicine and rehabilitation.
His work on Motor imagery and Sensorimotor rhythm as part of general Brain–computer interface research is frequently linked to Improved performance, thereby connecting diverse disciplines of science.
His studies in Artificial intelligence integrate themes in fields like Signal and Ulnar deviation.
His Pattern recognition research focuses on subjects like Proportional myoelectric control, which are linked to Artificial limbs.
His Audiology research incorporates themes from Somatosensory system, Neurorehabilitation and Affect.
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