Multi-Command Real-Time Brain Machine Interface Using SSVEP: Feasibility Study for Occipital and Forehead Sensor Locations

2008 ◽  
pp. 783-786 ◽  
Author(s):  
Pablo Martinez ◽  
Hovagim Bakardjian ◽  
Andrzej Cichocki
Keyword(s):  
Real Time ◽  
Feasibility Study ◽  
Brain Machine Interface ◽  
Machine Interface ◽  
Sensor Locations

scholarly journals A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

PLoS ONE ◽  
2009 ◽  
Vol 4 (12) ◽  
pp. e8218 ◽  
Author(s):  
Frank H. Guenther ◽  
Jonathan S. Brumberg ◽  
E. Joseph Wright ◽  
Alfonso Nieto-Castanon ◽  
Jason A. Tourville ◽  
...  
Keyword(s):  
Real Time ◽  
Speech Synthesis ◽  
Brain Machine Interface ◽  
Machine Interface

Motor Restoration Based on the Brain–Machine Interface Using Brain Surface Electrodes: Real-Time Robot Control and a Fully Implantable Wireless System

2012 ◽  
Vol 26 (3-4) ◽  
pp. 399-408 ◽  
Author(s):  
Masayuki Hirata ◽  
Kojiro Matsushita ◽  
Takufumi Yanagisawa ◽  
Tetsu Goto ◽  
Shayne Morris ◽  
...  
Keyword(s):  
Real Time ◽  
Robot Control ◽  
Brain Machine Interface ◽  
Wireless System ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Machine Interface ◽  
The Brain

scholarly journals Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface

2020 ◽  
Author(s):  
Samuel R. Nason ◽  
Matthew J. Mender ◽  
Alex K. Vaskov ◽  
Matthew S. Willsey ◽  
Parag G. Patil ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Cortical Neurons ◽  
High Speed ◽  
Neural Prostheses ◽  
Brain Machine Interface ◽  
Brain Machine Interfaces ◽  
Individual Finger ◽  
Machine Interface ◽  
Group Movements

SUMMARYModern brain-machine interfaces can return function to people with paralysis, but current hand neural prostheses are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a novel task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined, presenting separate targets for each group. During online brain control, the ReFIT Kalman filter demonstrated the capability of individuating movements of each finger group with high performance, enabling a nonhuman primate to acquire two targets simultaneously at 1.95 targets per second, resulting in an average information throughput of 2.1 bits per second. To understand this result, we performed single unit tuning analyses. Cortical neurons were active for movements of an individual finger group, combined movements of both finger groups, or both. Linear combinations of neural activity representing individual finger group movements predicted the neural activity during combined finger group movements with high accuracy, and vice versa. Hence, a linear model was able to explain how cortical neurons encode information about multiple dimensions of movement simultaneously. Additionally, training ridge regressing decoders with independent component movements was sufficient to predict untrained higher-complexity movements. Our results suggest that linear decoders for brain-machine interfaces may be sufficient to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

scholarly journals A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e59049 ◽  
Author(s):  
Maryam M. Shanechi ◽  
Ziv M. Williams ◽  
Gregory W. Wornell ◽  
Rollin C. Hu ◽  
Marissa Powers ◽  
...  
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Control Design ◽  
Optimal Feedback Control ◽  
Brain Machine Interface ◽  
Optimal Feedback ◽  
Machine Interface
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