Abstract
A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.
Highlights
Brain-computer interface (BCI) technology aims to establish a direct communication pathway between the brain and external devices
We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function
As proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and skill learning should be a useful metaphor to model BCI learning
Summary
Brain-computer interface (BCI) technology aims to establish a direct communication pathway between the brain and external devices. The central component of a BCI system is its neural decoder, a set of decoding weights that transform or map brain activity to behavior of an external device, e.g., robotic arm movement (Figure 1). The first is decoder calibration, where decoding weights are calculated based on brain activity and corresponding external device behavior data. The second process is BCI learning, where a BCI user learns the relationship between brain activity and resulting external device behavior given specific decoding weights. In another word, the user learns to generate specific cortical activity patterns for controlling external devices with the given decoding weights. The BCI learning process is much less understood To address this knowledge gap, this article will focus on BCI learning with two goals. On BCI systems that use implantable electrodes, such as ECoG and intracortical microelectrode arrays, with the goal of restoring motor function
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