Brain–machine interfaces: past, present and future

  title={Brain–machine interfaces: past, present and future},
  author={Mikhail A. Lebedev and Miguel A. L. Nicolelis},
  journal={Trends in Neurosciences},

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Real-time brain-machine interface architectures: neural decoding from plan to movement

A real-time BMI is developed that accurately and simultaneously decodes in advance a sequence of planned movements from neural activity in the premotor cortex and performs significantly better than linear regression approaches demonstrating the advantage of a design that more closely mimics the sensorimotor system.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation.

Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

It is shown that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI, and proposed that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.

Active tactile exploration enabled by a brain-machine-brain interface

The operation of a brain–machine–brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation of the primary somatosensory cortex is reported.

Building brain machine interfaces: From rat to monkey

This paper will review the lab's research work on invasive BMIs with subjects on rat and monkey, and shows the preliminary decoding results of the 2D trajectory, and plans to utilize the decoded prediction to control an external device, such as robot hand.


Compared to now, where prosthetics users are only able to use visual feedback to successfully maneuver their limbs, vibration-induced kinesthetic feedback gives amputees the ability to have accurate control with the sense of touch instead, thus improving their quality of life.

Active tactile exploration using a brain – machine – brain interface

The operation of a brain–machine–brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex is reported.

Brain-machine interfaces: an overview

Although noninvasive BMIs are safe and easy to implement, their information bandwidth is limited, and invasive BMIs hold promise to improve the bandwidth by utilizing multichannel recordings from ensembles of brain neurons.

Observation-based calibration of brain-machine interfaces for grasping

High decoding accuracies were obtained, demonstrating the feasibility of using action-observation as a calibration technique for decoding grasping movements, from noninvasively recorded electroencephalographic activity in human subjects.



Single neuron recording from motor cortex as a possible source of signals for control of external devices

  • E. Schmidt
  • Biology
    Annals of Biomedical Engineering
  • 2006
Improvements are required in electrode design, fabrication, implantation, and signal processing techniques before this method of obtaining control signals would be feasible for human applications, but preliminary studies have been encouraging on obtaining connections to the nervous system to control external devices.

Brain–computer-interface research: Coming of age

Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface

It is shown that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI, and that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces

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Brain-machine interface: Instant neural control of a movement signal

It is shown how activity from a few motor cortex neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace.

Brain–machine interfaces to restore motor function and probe neural circuits

It is proposed that functional, bidirectional, real-time interfaces between living brain tissue and artificial devices can become the core of a new experimental approach with which to investigate the operation of neural systems in behaving animals.

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The results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

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The results of a comprehensive analysis of the contribution of single cortical neurons to a simple linear model indicate that the experimental paradigm described here may be useful not only to investigate aspects of neural population coding, but it may also provide a test bed for the development of clinically useful cortical prosthetic devices aimed at restoring motor functions in severely paralyzed patients.