Solon Amelia J, Lawhern Vernon J, Touryan Jonathan, McDaniel Jonathan R, Ries Anthony J, Gordon Stephen M
Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States.
DCS Corporation, Alexandria, VA, United States.
Front Hum Neurosci. 2019 Jun 14;13:201. doi: 10.3389/fnhum.2019.00201. eCollection 2019.
Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.
深度卷积神经网络（CNN）此前已被证明是用于各种复杂领域（如图像处理和语音识别）中信号解码和分析的有用工具。通过从大量数据中学习，这些深度网络编码的表示通常对于基础特征空间中的适度变化具有不变性。最近，我们提出了一种可应用于脑电图（EEG）解码和分析的CNN架构。在本文中，我们使用先前实验的数据训练我们的CNN模型，以便稍后从未见过的、留出的实验中解码P300诱发反应。我们将CNN输出作为P300反应中基础变异性的函数进行分析，并证明CNN输出对实验引起的神经反应变化敏感。然后，我们评估我们的方法作为提高EEG记录中整体信噪比的手段的效用。最后，我们展示了一个基于CNN的解码如何应用于复杂数据分析的示例。
