Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, Sean; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

doi:10.1038/35042582

Cited by 1,284 publications

(941 citation statements)

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“…The accuracy of these predictions was comparable to that previously shown for kinematic signals (Serruya et al 2002, Wessberg et al 2000, Wu et al 2006 consistent with the emerging consensus that M1 contains both kinematic and kinetic (or force-related) information (Kakei et al 1999, Morrow et al 2007, Thach 1978. The EMG predictions remained stable within an experimental session and even across sessions for periods as long as two weeks.…”

Section: Discussionsupporting

confidence: 66%

“…The Carmena study is unusual in the number of different types of signals that were predicted. Among those studies considering only kinematics, there has been a broad range of prediction accuracy reported, some a bit greater than and some less than that reported by Carmena (Wessberg and Nicolelis 2004, Wessberg et al 2000, Serruya et al 2003, Wu et al 2006. The one other EMG prediction study of which we are familiar reported EMG prediction accuracy that ranged from r 2 = 0.29 to 0.74, with a mean of 0.56 (Santucci et al 2005).…”

Section: Prediction Of Kinetic Signalsmentioning

confidence: 95%

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Prediction of upper limb muscle activity from motor cortical discharge during reaching

et al. 2007

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Movement representation by the motor cortex (M1) has been a theoretical interest for many years, but in the past several years it has become a more practical question, with the advent of the brainmachine interface. An increasing number of groups have demonstrated the ability to predict a variety of kinematic signals on the basis of M1 recordings and to use these predictions to control the movement of a cursor or robotic limb. We, on the other hand, have undertaken the prediction of myoelectric (EMG) signals recorded from various muscles of the arm and hand during button pressing and prehension movements. We have shown that these signals can be predicted with accuracy that is similar to that of kinematic signals, despite their stochastic nature and greater bandwidth. The predictions were made using a subset of 12 or 16 neural signals selected in the order of each signal's unique, output-related information content. The accuracy of the resultant predictions remained stable through a typical experimental session. Accuracy remained above 80% of its initial level for most muscles even across periods as long as two weeks. We are exploring the use of these predictions as control signals for neuromuscular electrical stimulation in quadriplegic patients.

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Section: Discussionsupporting

confidence: 66%

Section: Prediction Of Kinetic Signalsmentioning

confidence: 95%

Prediction of upper limb muscle activity from motor cortical discharge during reaching

et al. 2007

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“…Recent evidence indicates that the PL cortex is directly involved in limbic/cognitive functions, homologous to the dorsolateral prefrontal cortex (DLPFC) in primates [21] , which is necessary for delayed-response tasks [2,22] . But and understanding the neuronal basis of behavior [23][24][25][26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

Anticipatory activity in rat medial prefrontal cortex during a working memory task

Bai

Liu

et al. 2012

Neurosci. Bull.

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Objective Working memory is a key cognitive function in which the prefrontal cortex plays a crucial role.This study aimed to show the firing patterns of a neuronal population in the prefrontal cortex of the rat in a working memory task and to explore how a neuronal ensemble encodes a working memory event. Methods Sprague-Dawley rats were trained in a Y-maze until they reached an 80% correct rate in a working memory task. Then a 16-channel microelectrode array was implanted in the prefrontal cortex. After recovery, neuronal population activity was recorded during the task, using the Cerebus data-acquisition system. Spatio-temporal trains of action potentials were obtained from the original neuronal population signals. Results During the Y-maze working memory task, some neurons showed significantly increased firing rates and evident neuronal ensemble activity. Moreover, the anticipatory activity was associated with the delayed alternate choice of the upcoming movement. In correct trials, the averaged pre-event firing rate (10.86 ± 1.82 spikes/ bin) was higher than the post-event rate (8.17 ± 1.15 spikes/bin) (P <0.05). However, in incorrect trials, the rates did not differ. Conclusion The results indicate that the anticipatory activity of a neuronal ensemble in the prefrontal cortex may play a role in encoding working memory events.

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“…Whenever the user induces a voluntary modification of these patterns, the BCI system is able to detect it and to translate it into an action that reflects the user's intent. Several animal and some human studies have shown the possibility to use electrical brain activity recorded within the brain to directly control the movement of robots or prosthetic devices in real time using microelectrodes implanted within the brain [6] [7][8] [9] [10]. Other BCI systems depend on brain activity recorded non-invasively from the surface of the scalp using electroencephalography (EEG).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive brain–computer interface system: Towards its application as assistive technology

Cincotti

Mattia

Aloise

et al. 2008

Brain Research Bulletin

266

147

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The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options.Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time.Correspondence should be addressed to: Febo Cincotti, PhD, Fondazione Santa Lucia, IRCCS, Via Ardeatina, 306, 00179, Rome, Italy, Phone: +39-06-51501466, Fax: +39-06-51501465, f.cincotti@hsantalucia.it. * These authors equally contributed to the paper Conflict of Interest Declaration: I had full access to all the data in the study and I take responsibility for the integrity of the data and the accuracy of the data analysis. I also state that all authors have read and approved submission of the manuscript; the manuscript contains original material that has not been published and has not being considered for publication elsewhere.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. NIH Public Access

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Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

Cited by 1,284 publications

References 26 publications

Prediction of upper limb muscle activity from motor cortical discharge during reaching

Prediction of upper limb muscle activity from motor cortical discharge during reaching

Anticipatory activity in rat medial prefrontal cortex during a working memory task

Non-invasive brain–computer interface system: Towards its application as assistive technology

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