Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex

Chapin, John K.; Moxon, Karen A.; Markowitz, Ronald S.; Nicolelis, Miguel A. L.

doi:10.1038/10223

Cited by 990 publications

(519 citation statements)

References 32 publications

Supporting

Mentioning

482

Contrasting

Unclassified

Order By: Relevance

“…The brain-machine interface field has been dominated by applications in which neural signals recorded from motor or premotor areas are used to predict kinematic signals: the position of a cursor on a computer screen (Serruya et al 2002, Taylor et al 2002, Santhanam et al 2006, Kennedy and Bakay 1998, Wolpaw and McFarland 2004 or the endpoint of a robotic limb (Serruya et al 2002, Chapin et al 1999, Taylor et al 2003, Carmena et al 2003. We have demonstrated that similar techniques can be used to predict the activity of individual muscles of the arm and hand.…”

Section: Prediction Of Kinetic Signalsmentioning

confidence: 99%

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

et al. 2007

View full text Add to dashboard Cite

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.

show abstract

Section: Prediction Of Kinetic Signalsmentioning

confidence: 99%

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

et al. 2007

View full text Add to dashboard Cite

show abstract

“…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

270

147

View full text Add to dashboard Cite

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

show abstract

“…More recent work reports similar memory activity in parietal cortex before reaches (Snyder et al, 1997) and grasps (Sakata et al, 1995) relating parietal cortex to movement plans in general. There is great interest in helping paralyzed patients by using cortical SU activity to control a prosthesis (Kennedy and Bakay, 1998;Chapin et al, 1999) and the relation of parietal SU activity to movement planning makes it a potentially useful signal (Shenoy et al, 1999). However, the difficulty of isolating SU activity with cortical implants has presented a major hurdle to the development of a neural prosthesis.…”

mentioning

confidence: 99%

Temporal structure in neuronal activity during working memory in macaque parietal cortex

et al. 2002

View full text Add to dashboard Cite

A number of cortical structures are reported to have elevated single unit firing rates sustained throughout the memory period of a working memory task. How the nervous system forms and maintains these memories is unknown but reverberating neuronal network activity is thought to be important. We studied the temporal structure of single unit (SU) activity and simultaneously recorded local field potential (LFP) activity from area LIP in the inferior parietal lobe of two awake macaques during a memory-saccade task. Using multitaper techniques for spectral analysis, which play an important role in obtaining the present results, we find elevations in spectral power in a 50-90Hz (gamma) frequency band during the memory period in both SU and LFP activity. The activity is tuned to the direction of the saccade providing evidence for temporal structure that codes for movement plans during working memory. We also find SU and LFP activity are coherent during the memory period in the 50-90Hz gamma band and no consistent relation is present during simple fixation. Finally, we find organized LFP activity in a 15-25Hz frequency band that may be related to movement execution and preparatory aspects of the task. Neuronal activity could be used to control a neural prosthesis but SU activity can be hard to isolate with cortical implants. As the LFP is easier to acquire than SU activity, our finding of rich temporal structure in LFP activity related to movement planning and execution may accelerate the development of this medical application.Keywords: parietal, prosthesis, local field potential, gamma band, coherence, temporal structure.Pesaran et. al. 3Working memory is a brain system requiring the temporary storage and manipulation of information necessary for the performance of complex cognitive tasks (Baddeley, 1992). The neurophysiological basis of working memory is studied in non-human primates by recording neural activity during delayed-response tasks (Fuster, 1995). Cue-selective elevated single unit firing rates have been recorded during the delay period in many brain areas during different versions of the task (Fuster and Jervey, 1982;Bruce and Goldberg, 1985;Gnadt and Andersen, 1988;Miyashita and Chang, 1988;Funahashi et al., 1989;Koch and Fuster, 1989;Miller et al., 1996;Zhou and Fuster, 1996). How this neural activity is sustained is unknown but may be important to understanding the neural basis of working memory (Goldman-Rakic, 1995). Converging evidence points to the importance of a distributed recurrent neuronal network (Goldman-Rakic, 1988) and reverberating network activity has long been suggested as a possible mechanism for short-term memory (Lorente de No, 1938;Hebb, 1949;Amit, 1995;Seung, 1996;Wang, 1999).Measures with the potential to capture correlated neural activity on a millisecond time scale may be needed to resolve reverberating memory activity. The dynamical structure of neuronal activity has been the source of much interest as a temporal code (for a review see Singer and Gray (1995) ) however ...

show abstract

Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex

Cited by 990 publications

References 32 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

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

Temporal structure in neuronal activity during working memory in macaque parietal cortex

Contact Info

Product

Resources

About