Abstract
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 (mathematical ways to pick patterns out of complex data) ought to discover the individual brain patterns characterizing the mental tasks executed by the user. In essence, a BMI is a two-learner system.
Highlights
Brain–machine interfaces (BMIs), or brain–computer interfaces, are an exciting multidisciplinary field that has grown tremendously during the last decade
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
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 ought to discover the individual brain patterns characterizing the mental tasks executed by the user
Summary
Brain–machine interfaces (BMIs), or brain–computer interfaces, are an exciting multidisciplinary field that has grown tremendously during the last decade. 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. 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 (mathematical ways to pick patterns out of complex data) ought to discover the individual brain patterns characterizing the mental tasks executed by the user.
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