论文标题
BCI墙：一种可预测大脑计算机界面成功或失败的强大方法
BCI-Walls: A robust methodology to predict success or failure in brain computer interfaces
论文作者
论文摘要
大脑计算机接口（BCI）取决于可靠的实时检测有意识的脑电图更改，例如控制视频游戏。但是，头皮记录被非平稳噪声污染，例如面部肌肉活动和眼睛运动。这会干扰检测过程，从而使其潜在不可靠甚至不可能。我们已经开发了一种新的方法，如果在存在非平稳噪声的情况下可以检测到有意识的脑电图变化，则可以通过要求头皮记录的信噪比大于SNR壁，而这又基于记录的最高和最低的噪声方差。作为一个教学示例，我们在八种不同的活动中录制了中央电极CZ的信号，导致非平稳噪音，例如玩电子游戏或大声读出。结果表明，面部肌肉活动和眼动作用对脑电图的可检测性具有很大的影响，并且最小化眼动伪影和肌肉噪声对于能够检测有意识的脑电图变化至关重要。
Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.
