What is he best known for?
The fields of study he is best known for:
- Neuroscience
- Artificial intelligence
- Neuron
His primary scientific interests are in Motor cortex, Primary motor cortex, Anatomy, Neuroscience and Wrist.
His Motor cortex research focuses on Electromyography and how it relates to Motor system, Human–computer interaction and Degrees of freedom.
His Primary motor cortex research incorporates elements of Motor coordination, Cognitive psychology, Flexibility and Motor learning.
His Anatomy study which covers Finger movement that intersects with Stimulation.
His study in Neuroscience is interdisciplinary in nature, drawing from both Thumb, Hemiparesis and Weakness.
His work carried out in the field of Wrist brings together such families of science as Cerebral cortex, Upper limb, Cortical surface and Forearm.
His most cited work include:
- Constraints on somatotopic organization in the primary motor cortex. (435 citations)
- How somatotopic is the motor cortex hand area (352 citations)
- Quantifying the Independence of Human Finger Movements: Comparisons of Digits, Hands, and Movement Frequencies (325 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Neuroscience, Primary motor cortex, Motor cortex, Artificial intelligence and Anatomy.
Marc H. Schieber has researched Primary motor cortex in several fields, including Central sulcus, Premovement neuronal activity, Finger movement, Premotor cortex and Posterior parietal cortex.
Marc H. Schieber works mostly in the field of Motor cortex, limiting it down to concerns involving Thumb and, occasionally, Index finger.
The concepts of his Artificial intelligence study are interwoven with issues in Decoding methods, Neuroprosthetics, Computer vision and Pattern recognition.
His research integrates issues of Speech recognition and Brain–computer interface in his study of Decoding methods.
Marc H. Schieber studies Anatomy, namely Wrist.
He most often published in these fields:
- Neuroscience (43.17%)
- Primary motor cortex (28.78%)
- Motor cortex (27.34%)
What were the highlights of his more recent work (between 2012-2021)?
- Neuroscience (43.17%)
- Artificial intelligence (23.02%)
- Primary motor cortex (28.78%)
In recent papers he was focusing on the following fields of study:
Marc H. Schieber mostly deals with Neuroscience, Artificial intelligence, Primary motor cortex, Pattern recognition and Motor cortex.
The Artificial intelligence study combines topics in areas such as Communication, Decoding methods, Neural decoding and Computer vision.
His Communication research is multidisciplinary, relying on both Physical medicine and rehabilitation, Finger tapping and Motor learning.
His Primary motor cortex research incorporates themes from Premotor cortex, Functional magnetic resonance imaging, Brain–computer interface and Finger flexion.
In his work, Encoding, Primate and Anatomy is strongly intertwined with Premovement neuronal activity, which is a subfield of Premotor cortex.
He undertakes interdisciplinary study in the fields of Motor cortex and Precentral gyrus through his works.
Between 2012 and 2021, his most popular works were:
- State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. (62 citations)
- Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. (25 citations)
- Spatiotemporal Distribution of Location and Object Effects in Primary Motor Cortex Neurons during Reach-to-Grasp. (23 citations)
In his most recent research, the most cited papers focused on:
- Neuroscience
- Artificial intelligence
- Neuron
Marc H. Schieber mainly investigates Artificial intelligence, Primary motor cortex, Neuroscience, Computer vision and Communication.
His Artificial intelligence research includes themes of Neuroprosthetics, State space, Set and Pattern recognition.
Neuroscience is closely attributed to Premotor cortex in his work.
His work investigates the relationship between Computer vision and topics such as Trajectory that intersect with problems in Robot, Kalman filter, Neural decoding and Object detection.
His research combines Motor cortex and Communication.
His Neuron study combines topics from a wide range of disciplines, such as Electromyography, Wrist, Reticular formation and Spike-triggered average.
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