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

@article{Serruya2002BrainmachineII,
  title={Brain-machine interface: Instant neural control of a movement signal},
  author={Mijail D. Serruya and Nicholas G. Hatsopoulos and Liam Paninski and Matthew R. Fellows and John P. Donoghue},
  journal={Nature},
  year={2002},
  volume={416},
  pages={141-142}
}
The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices, but until now this has called for extensive training or has been confined to a limited movement repertoire. [] Key Result Our results, which are based on recordings made by an electrode array that is suitable for human use, indicate that neurally based control of movement may eventually be feasible in paralysed humans.

Neural Correlates of Learning in Brain Machine Interface Controlled Tasks

The results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions.

Cortically controlled brain-machine interface

It is demonstrated that primary motor cortical activity may be optimized for continuous movement control whereas signals from the premotor cortex may be better suited for discrete target selection.

Current challenges in the development of a cortical brain-machine interface

  • N. HatsopoulosJ. O'LearyJ. Joshi
  • Biology
    Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)
  • 2003
Results are presented that attempt to address challenges based on multi-electrode recording from multiple motor cortical areas in behaving monkeys by focusing on the most suitable electrophysiological signal for practical use in a BMI.

Improving brain–machine interface performance by decoding intended future movements

This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user's future intent can compensate for the negative effect of control delay on BMI performance.

Free-paced high-performance brain–computer interfaces

The design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone are reported, suggesting that a completely neurally-driven high-performance brain–computer interface is possible.

Neural control of motor prostheses

Neural prosthetic control signals from plan activity

A computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements is performed to demonstrate how control signals can be derived from such plan activity.

Development of an invasive brain-machine interface with a monkey model

An invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.

Analysis of neural activity in human motor cortex -- Towards brain machine interface system

The results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface and the influence of movement context on movement reconstruction accuracy.
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