High-Accuracy Brain-Machine Interfaces Using Feedback Information

High-Accuracy Brain-Machine Interfaces Using Feedback Information

Sensory feedback is very important for movement control. However, feedback information has not been directly used to update movement prediction model in the previous BMI studies, although the closed-loop BMI system provides the visual feedback to users. Here, we propose a BMI framework combining image processing as the feedback information with a novel prediction method. The feedback-prediction...

A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily...

Non invasive Brain-Machine Interfaces

This document represents the final report of the study " Non invasive brain-machine interfaces " performed by the Interdepartmental Research Center " E. Piaggio " of the University of Pisa within the ARIADNA framework of activities promoted by the European Space Agency (ESA). Contents of the report are organized as follows. The first part presents a literature survey on the state of the art of ...

Non Invasive Brain-Machine Interfaces

Brain–Machine Interfaces: Your Brain in Action

An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, each being associated to a particular intention or mental task. Hence, adaptation is a key component of a BMI, because, on the one side, users must learn to control their neural activity so as to generate distinct brain patterns, while, on the other side, machine learning techniques (math...