论文标题
使用干性脑电图和神经网络进行快速的单次在线在线ERP脑部计算机界面：一项试验研究
Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study
论文作者
论文摘要
加快与事件相关电位（ERP）的脑部计算机界面（BCI）中的拼写，需要在短时间内引起强大的大脑反应，就像对此类诱发电位的准确分类保持挑战性，并对信号处理和机器学习技术施加了硬约束。刺激表现和深度学习的最新进展展示了有望显着提高这些系统疗效的有希望的方向，在本研究中，我们提出了在干燥电极和快速闪烁的单次ERP BCI中，使用卷积神经网络使用卷积神经网络将有色倒置的面部刺激与分类结合在一起。达到高的在线准确性，两个受试者通过90％的正确符号检测栏，转移率高于每分钟60位，这表明了改善基于ERP的BCI的实用性的方法。
Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked potentials remains challenging and imposes hard constraints for signal processing and machine learning techniques. Recent advances in stimulus presentation and deep learning showcased a promising direction in significantly improving the efficacy of those systems, in this study we propose the combination of colored inverted face stimulation with classification using convolutional neural networks in the hard settings of dry electrodes and fast flashing single-trial ERP-based BCI. The high online accuracy achieved, with two subjects passing the 90 percent correct symbol detection bar and a transfer rate above 60 bits per minute, demonstrates the approach potential in improving the practicality of ERP based BCIs.
