A high-performance brain–computer interface

By G. Santhanam, Stephen I. Ryu, Byron M. Yu, A. Afshar, Krishna V. Shenoy. Nature, 2006

718 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dd029cfe60e00f4f31dd84bcf49c90fbca893d4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Intention, action planning, and decision making in parietal-frontal circuits. (2009, methodology): “To this point, sequential goals have been decoded in brain control tasks using PMd activity (Santhanam et al., 2006).”
Bridging the brain to the world: a perspective on neural interface systems. (2008, positive): “Shenoy and colleagues (Achtman et al., 2007; Santhanam et al., 2006 ) have shown that premotor cortex (PM) in monkeys contains information about target goals that can be decoded as discrete selections, akin to keyboard entry, during movement planning.”
The Science of Neural Interface Systems (2009, neutral): “This was followed by a number of studies (Carmena et al. 2003, Musallam et al. 2004, Santhanam et al. 2006, Serruya et al. 2002, Taylor et al. 2002) showing that closed-loop control in primates is possible.”
Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface (2013, background): “…of BCIs where intracranial electrophysiological signals are recorded and interpreted to decode the intent of subjects and control external devices (Georgopoulos et al 1982, Taylor et al 2002, Musallam et al 2004, Hochberg et al 2006, Santhanam et al 2006, Velliste et al 2008, Hochberg et al 2012).”
High-performance brain-to-text communication via handwriting (2021, background): “Other discrete (classification-based) BCIs have also typically used directional movements with little temporal variety, which may have limited their accuracy and/or the size of the movement set 24,25 .”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, methodology): “For the discrete decoder, a naive Bayes classifier was used, similar to one we have used previously [2].”
Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia (2008, background): “…primates have demonstrated that extracellular neural activity from a population of neurons in the motor areas of cerebral cortex can be converted into a continuous control signal for the operation of computers or robotic devices [1] [2] [3] [4] [5] [6] [7] [8] (see [9] [10] [11] [12] for review).”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, background): “…et al., 2013 ), and intracortical electrical signals ( Serruya et al., 2002 ; Taylor et al., 2002 ; Carmena et al., 2003 ; Musallam et al., 2004 ; Santhanam et al., 2006 ; Hochberg et al., 2006 ; Ganguly et al., 2011 ; O’Doherty et al., 2011 ; Gilja et al., 2012 ; Simeral et al., 2011 ; Bacher…”
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm (2012, neutral): “Future clinical research will be needed to establish whether more signals 19–22 , signals from additional or other areas 2,23–25 , better decoders, explicit participant training, or other advances (see Supplementary Materials) will provide more complex, flexible, independent, and natural control.”