Abstract
A brain–computer interface (BCI) is a computer-based system that acquires, analyzes, and translates brain signals into output commands in real time. Perdikis and colleagues demonstrate superior performance in a Cybathlon BCI race using a system based on “three pillars”: machine learning, user training, and application. These results highlight the fact that BCI use is a learned skill and not simply a matter of “mind reading.”
Highlights
People can learn through a training protocol to increase or decrease sensorimotor rhythm (SMR) amplitude and can use this control to move a computer cursor or operate another device [8,9,10]
The fact that brain–computer interface (BCI) use is a skill has been emphasized previously, and Perdikis and colleagues [19] acknowledge that their conclusions are limited by the fact that they are based on an uncontrolled study of only two individuals
It is often assumed that the way to train users with a sensorimotor rhythm (SMR)-based BCI is to ask them to generate specific mental states through motor imagery [18]
Summary
People can learn through a training protocol to increase or decrease SMR amplitude and can use this control to move a computer cursor or operate another device [8,9,10]. The BCI user learns to encode his/her intent in brain signal features (e.g., SMRs) that the BCI can record, extract, and translate into output commands. Many BCI studies have largely ignored the user-training component of BCI operation.
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