Objective. A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features and is widely used in classification tasks with scalp electroencephalogram (EEG) signals. However, the small number of samples and low signal-to-noise ratio involved in scalp EEG with low spatial resolution constitute a limitation that might restrict potential brain-computer interface (BCI) applications that are based on the CNN model. In the present study, a novel CNN model with source-spatial feature images (SSFIs) as the input is proposed to decode auditory attention tracking states in a cocktail party environment. Approach. We first extract SSFIs using rhythm entropy and weighted minimum norm estimation. Next, we develop a CNN model with three convolutional layers. Furthermore, we estimate the performance of the proposed model via generalized performance, alternative models that deleted or replaced a model’s compo...

中文翻译：

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鸡尾酒会环境中的听觉注意力跟踪状态可以通过深度卷积神经网络进行解码。

目的。深度卷积神经网络（CNN）是一种用于深度学习（DL）的方法。它具有自动提取特征的强大功能，并广泛用于带有头皮脑电图（EEG）信号的分类任务。然而，具有低空间分辨率的头皮脑电图所涉及的少量样本和低信噪比构成了局限性，可能会限制基于CNN模型的潜在脑机接口（BCI）应用程序。在本研究中，提出了一种以源空间特征图像（SSFI）为输入的新型CNN模型，以对鸡尾酒会环境中的听觉注意力跟踪状态进行解码。方法。我们首先使用节奏熵和加权最小范数估计来提取SSFI。接下来，我们开发具有三个卷积层的CNN模型。此外，