Cognitive neural prosthetics (original) (raw)
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Decoding Movement Intent From Human Premotor Cortex Neurons for Neural Prosthetic Applications
Journal of Clinical Neurophysiology, 2006
Primary motor cortex (M1), a key region for voluntary motor control, has been considered a first choice as the source of neural signals to control prosthetic devices for humans with paralysis. Less is known about the potential for other areas of frontal cortex as prosthesis signal sources. The frontal cortex is widely engaged in voluntary behavior. Single-neuron recordings in monkey frontal cortex beyond M1 have readily identified activity related to planning and initiating movement direction, remembering movement instructions over delays, or mixtures of these features. Human functional imaging and lesion studies also support this role. Intraoperative mapping during deep brain stimulator placement in humans provides a unique opportunity to evaluate potential prosthesis control signals derived from nonprimary areas and to expand our understanding of frontal lobe function and its role in movement disorders. This study shows that recordings from small groups of human prefrontal/premotor cortex neurons can provide information about movement planning, production, and decision-making sufficient to decode the planned direction of movement. Thus, additional frontal areas, beyond M1, may be valuable signal sources for human neuromotor prostheses.
2010
Summary: Primary motor cortex (M1), a key region for voluntary motor control, has been considered a first choice as the source of neural signals to control prosthetic devices for humans with paralysis. Less is known about the potential for other areas of frontal cortex as prosthesis signal sources. The frontal cortex is widely engaged in voluntary behavior. Single-neuron recordings in monkey frontal cortex beyond M1 have readily identified activity related to planning and initiating movement direction, remembering movement instructions over delays, or mixtures of these features. Human functional imaging and lesion studies also support this role. Intraoperative mapping during deep brain stimulator placement in humans provides a unique opportunity to evaluate potential prosthesis control signals derived from nonprimary areas and to expand our understanding of frontal lobe function and its role in movement disorders. This study shows that recordings from small groups of human prefronta...
Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human
Science (New York, N.Y.), 2015
Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.
Neural prosthetic control signals from plan activity
NeuroReport, 2003
The prospect of assisting disabled patients by translating neural activity from the brain into control signals for prosthetic devices, has £ourished in recent years. Current systems rely on neural activity present during natural arm movements. We propose here that neural activity present before or even without natural arm movements can provide an important, and potentially advantageous, source of control signals. To demonstrate how control signals can be derived from such plan activity we performed a computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements. We employed maximum likelihood decoders to estimate movement direction and to drive ¢nite state machines governing when to move. Performance exceeded 90% with as few as 40 neurons. NeuroRe-port12 :000^000
Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles
Journal of Neurophysiology
rons in the lateral prefrontal cortex (LPFC) encode sensory and cognitive signals, as well as commands for goal-directed actions. Therefore, the LPFC might be a good signal source for a goal-selection brain-computer interface (BCI) that decodes the intended goal of a motor action previous to its execution. As a first step in the development of a goal-selection BCI, we set out to determine if we could decode simple behavioral intentions to direct gaze to eight different locations in space from single-trial LPFC neural activity. We recorded neuronal spiking activity from microelectrode arrays implanted in area 8A of the LPFC of two adult macaques while they made visually guided saccades to one of eight targets in a center-out task. Neuronal activity encoded target location immediately after target presentation, during a delay epoch, during the execution of the saccade, and every combination thereof. Many (40%) of the neurons that encoded target location during multiple epochs preferred different locations during different epochs. Despite heterogeneous and dynamic responses, the neuronal feature set that best predicted target location was the averaged firing rates from the entire trial and it was best classified using linear discriminant analysis (63.6-96.9% in 12 sessions, mean 80.3%; information transfer rate: 21-59, mean 32.8 bits/min). Our results demonstrate that it is possible to decode intended saccade target location from single-trial LPFC activity and suggest that the LPFC is a suitable signal source for a goal-selection cognitive BCI. brain-computer interface; Macaca fascicularis; neural trajectory; prefrontal cortex INDIVIDUALS with motor impairments following central nervous system trauma or disease may use a brain-computer interface (BCI) to translate the electric signals from the brain into prosthetic limb control (Aflalo et al. 2015; Collinger et al. 2013; Hochberg et al. 2006; Wang et al. 2013). Most neuroprosthetic BCIs continuously decode effector trajectories from the activity of a population of neurons in motor cortex (Hatsopoulos and Donoghue 2009; Pohlmeyer et al. 2007; Schwartz 2004). Alternatively, neural signals in upstream brain areas from motor cortex that are engaged in cognitive aspects of movement such as attention, decision-making, planning, learning, memory, expected reward, and visuomotor transformation may be used to drive a goal-selection BCI (Andersen et
A framework for relating neural activity to freely moving behavior
2011
Abstract—Two research communities, motor systems neuroscience and motor prosthetics, examine the relationship between neural activity in the motor cortex and movement. The former community aims to understand how the brain controls and generates movement; the latter community focuses on how to decode neural activity as control signals for a prosthetic cursor or limb. Both have made progress toward understanding the relationship between neural activity in the motor cortex and behavior.
Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics
Neuron, 2006
Brain-controlled interfaces are devices that capture brain transmissions involved in a subject's intention to act, with the potential to restore communication and movement to those who are immobilized. Current devices record electrical activity from the scalp, on the surface of the brain, and within the cerebral cortex. These signals are being translated to command signals driving prosthetic limbs and computer displays. Somatosensory feedback is being added to this control as generated behaviors become more complex. New technology to engineer the tissue-electrode interface, electrode design, and extraction algorithms to transform the recorded signal to movement will help translate exciting laboratory demonstrations to patient practice in the near future.
Decoding of plan and peri-movement neural signals in prosthetic systems
IEEE Workshop on Signal Processing Systems, 2002
One might anticipate that combining plan activity with peri-In this paper we introduce a theoretical framework for improved processing of peri-movement neural activity for neurally controlled prosthetic systems through maximum likelihood sequence estimation. This framework further suggests a computational method for integrating plan and peri-movement neural activity. We show that combining plan activity, usually associated with target specification, with peri-movement neural activity yields more accurate estimates of the trajectoryof an arm movement. The effectiveness of the method is demonstrated in simulation. Performance as a function of the specific number of plan and peri-movement neurons, as well as other system and design parameters is analyzed. The algorithm presented is also compared against previous, sample-based approaches, specifically a "point-process'' filter for plan activity and a standard linear filter framework in the perimovement regime.