World’s fastest brain-computer interface: Combining EEG2Code with deep learning
World’s fastest brain-computer interface: Combining EEG2Code with deep learning
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DOI：
10.1101/546986
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发表时间：
2019-02
期刊：
影响因子：
3.7
通讯作者：
S. Nagel;M. Spüler
中科院分区：
文献类型：
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作者：
S. Nagel;M. Spüler
在本文中，我们提出了一个脑-机接口（BCI），能够达到超过1200位/分钟的信息传输速率（ITR）使用非侵入性记录的EEG信号。通过将EEG 2Code方法与深度学习相结合，我们提出了一种非常强大的方法来解码EEG中的视觉信息。这种方法可以在被动BCI设置中用于预测人正在观看的视觉刺激的属性，或者可以用于主动控制BCI拼写应用。所提出的方法进行了测试，在这两种情况下，实现了平均ITR为701位/分钟的被动BCI方法与最好的主题实现了在线ITR为1237位/分钟。所提出的BCI是比以前最快的BCI快三倍以上，并允许歧视500，000不同的视觉刺激的基础上2秒的EEG数据的准确性高达100%。当在异步BCI中使用该方法进行拼写时，我们实现了175 bit/min的平均利用率，相当于平均每分钟35个无错误字母。当我们观察到天花板效应时，更强大的脑信号解码方法不再转化为更好的BCI控制，我们讨论了BCI研究是否已经达到了非侵入性BCI控制性能无法大幅提高的地步。
In this paper, we present a Brain-Computer Interface (BCI) that is able to reach an information transfer rate (ITR) of more than 1200 bit/min using non-invasively recorded EEG signals. By combining the EEG2Code method with deep learning, we present an extremely powerful approach for decoding visual information from EEG. This approach can either be used in a passive BCI setting to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI spelling application. The presented approach was tested in both scenarios and achieved an average ITR of 701 bit/min in the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. The presented BCI is more than three times faster than the previously fastest BCI and allows to discriminate 500,000 different visual stimuli based on 2 seconds of EEG data with an accuracy of up to 100 %. When using the approach in an asynchronous BCI for spelling, we achieved an average utility rate of 175 bit/min, which corresponds to an average of 35 error-free letters per minute. As we observe a ceiling effect where more powerful approaches for brain signal decoding do not translate into better BCI control anymore, we discuss if BCI research has reached a point where the performance of non-invasive BCI control cannot be substantially improved anymore.