What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Neuroscience
His main research concerns Artificial intelligence, Neuroscience, Electroencephalography, Speech recognition and Computer vision.
His work carried out in the field of Artificial intelligence brings together such families of science as Decoding methods, Task, Brain–computer interface and Pattern recognition.
His study explores the link between Neuroscience and topics such as Biomedical engineering that cross with problems in Speckle pattern.
His Electroencephalography course of study focuses on Asphyxia and Electrophysiology.
He works mostly in the field of Speech recognition, limiting it down to concerns involving Algorithm and, occasionally, Wavelet, Ventricular tachycardia and Ventricular fibrillation.
His Computer vision study integrates concerns from other disciplines, such as Endoscope and Biomechanics.
His most cited work include:
- Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection (712 citations)
- Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition. (679 citations)
- Electric field and stimulating influence generated by deep brain stimulation of the subthalamic nucleus (464 citations)
What are the main themes of his work throughout his whole career to date?
Nitish V. Thakor mainly investigates Artificial intelligence, Electroencephalography, Neuroscience, Biomedical engineering and Pattern recognition.
Nitish V. Thakor interconnects Machine learning, Decoding methods and Computer vision in the investigation of issues within Artificial intelligence.
His biological study spans a wide range of topics, including Anesthesia, Asphyxia and Speech recognition.
Anesthesia is closely attributed to Ischemia in his research.
He has included themes like Signal and Signal processing in his Speech recognition study.
Neurophysiology, Stimulation, Somatosensory evoked potential, Somatosensory system and Thalamus are among the areas of Neuroscience where the researcher is concentrating his efforts.
He most often published in these fields:
- Artificial intelligence (22.39%)
- Electroencephalography (18.56%)
- Neuroscience (18.83%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (22.39%)
- Electroencephalography (18.56%)
- Biomedical engineering (14.08%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Electroencephalography, Biomedical engineering, Stimulation and Neuroscience.
His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition.
His work deals with themes such as Cognition, Sensory stimulation therapy, Task analysis and Perception, which intersect with Electroencephalography.
The Biomedical engineering study combines topics in areas such as Photoacoustic imaging in biomedicine, Contrast, Ultrasound, Triboelectric effect and Actuator.
His study looks at the intersection of Stimulation and topics like Prosthesis with Electromyography and Pattern recognition.
All of his Neuroscience and Sensory system and Inhibitory postsynaptic potential investigations are sub-components of the entire Neuroscience study.
Between 2017 and 2021, his most popular works were:
- Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. (96 citations)
- Polymer-based composites by electrospinning: Preparation & functionalization with nanocarbons (77 citations)
- Polymer-based composites by electrospinning: Preparation & functionalization with nanocarbons (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Neuroscience
His primary areas of study are Biomedical engineering, Stimulation, Artificial intelligence, Electroencephalography and Photon upconversion.
His Biomedical engineering study combines topics from a wide range of disciplines, such as Muscle Stimulation, Triboelectric effect, Electrical muscle stimulation, Magnetic resonance imaging and Actuator.
His research integrates issues of Peripheral nerve and Urology in his study of Stimulation.
His studies link Pattern recognition with Artificial intelligence.
His Electroencephalography study incorporates themes from Spectral density, Degree, Algorithm, Sample entropy and Discriminative model.
His study in Photon upconversion is interdisciplinary in nature, drawing from both Biological neural network and Light attenuation.
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