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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

Matthew D Golub¹, Steven M Chase², Aaron P Batista³

¹Department of Electrical and Computer Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States.

Current Opinion in Neurobiology

|January 23, 2016

Summary

This summary is machine-generated.

Brain-computer interfaces (BCIs) offer a simplified model for studying sensorimotor control. BCIs reveal cognitive processes like prediction and learning, advancing our understanding of neural mechanisms.

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Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Sensorimotor control involves complex cognitive processes like prediction and learning.
Studying these processes via arm reaching is difficult due to limited neural recordings, complex arm dynamics, and multiple sensory inputs.
Brain-computer interfaces (BCIs) provide a simplified yet relevant model system.

Purpose of the Study:

To highlight the advantages of BCIs for fundamental research in sensorimotor control.
To review how BCIs have advanced understanding of neural mechanisms in sensorimotor control.

Main Methods:

Review of recent scientific literature on brain-computer interface studies.
Analysis of how BCIs address challenges in studying sensorimotor control (e.g., limited neural data, system complexity).

Main Results:

BCIs simplify the sensorimotor loop, facilitating the study of neural mechanisms.
BCIs engage cognitive processes similar to natural movement, including prediction and learning.
Recent BCI research has yielded novel insights into sensorimotor control.

Conclusions:

BCIs are valuable tools for basic scientific investigation of sensorimotor control.
The simplified nature of BCIs aids in deciphering complex neural processes underlying movement and cognition.