Results 31 to 40 of about 388,034 (342)

State Variables of the Arm May Be Encoded by Single Neuron Activity in the Monkey Motor Cortex [PDF]

, 2016
Revealing the type of information encoded by neurons activity in the motor cortex is essential not only for understanding the mechanism of motion control but also for developing a brain-machine interface.
Eizo Miyashita, Yutaka Sakaguchi
core   +2 more sources

A Fully Integrated Sensor-Brain–Machine Interface System for Restoring Somatosensation [PDF]

IEEE Sensors Journal, 2020
Sensory feedback is critical to the performance of neural prostheses that restore movement control after neurological injury. Recent advances in direct neural control of paralyzed arms present new requirements for miniaturized, low-power sensor systems ...
Xilin Liu, Hongjie Zhu, Tian Qiu, Srihari Y. Sritharan, Dengteng Ge, Shu Yang, Milin Zhang, A. Richardson, T. Lucas, N. Engheta, J. van der Spiegel   +10 more
semanticscholar   +1 more source

Brain-to-brain communication during musical improvisation: a performance case study [version 3; peer review: 1 approved, 2 approved with reservations]

F1000Research, 2023
Understanding and predicting others' actions in ecological settings is an important research goal in social neuroscience. Here, we deployed a mobile brain-body imaging (MoBI) methodology to analyze inter-brain communication between professional musicians
Mauricio A. Ramírez-Moreno, José L. Contreras-Vidal, Dan Gelok, Guillermo Reza, Girija Chatufale, Woody Witt, Jesús G. Cruz-Garza, Akanksha Acharya   +7 more
doaj   +1 more source

Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence

Frontiers in Human Neuroscience, 2020
Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration ...
Jesus G. Cruz-Garza, Akshay Sujatha Ravindran, Anastasiya E. Kopteva, Cristina Rivera Garza, Cristina Rivera Garza, Jose L. Contreras-Vidal   +5 more
doaj   +1 more source

Artificial Brains and Hybrid Minds [PDF]

, 2017
The paper develops two related thought experiments exploring variations on an ‘animat’ theme. Animats are hybrid devices with both artificial and biological components.
A Clark, A Turing, D Chalmers, D Dennett, D Dennett, D Dennett, J Fodor, J Searle, N Block, N Block, P Churchland, P Schweizer, P Schweizer, PN Johnson-Laird, R French, R Jackendoff, R Rupert, S Franklin, S Harnad, WG Lycan   +19 more
core   +3 more sources

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth

Scientific Reports, 2023
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In
Akshay Sujatha Ravindran, Jose Contreras-Vidal   +1 more
doaj   +1 more source

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

PLoS Biology, 2003
Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain ...
J. Carmena, M. Lebedev, R. Crist, Joseph E. O’Doherty, David M. Santucci, Dragan F. Dimitrov, Parag G. Patil, C. Henriquez, M. Nicolelis   +8 more
semanticscholar   +1 more source

Brain-computer interface [PDF]

, 2011
A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device.
Marchenko, D.O., Sirobaba, N.S.
core   +1 more source

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements [PDF]

, 2013
Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function.
Fagg, Andrew H., Hatsopoulos, Nicholas G., Suminski, Aaron J., Willett, Francis R.   +3 more
core   +2 more sources

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach. [PDF]

, 2016
Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models.
Bigdely-Shamlo, Nima, Makeig, Scott, Robbins, Kay A   +2 more
core   +1 more source