In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.

中文翻译：

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基于脑电图谱时间特征的卷积神经网络心算任务分类

近年来，神经科学家对脑机接口（BCI）设备的开发很感兴趣。患有运动障碍的患者可能会受益于 BCI 作为一种交流方式和恢复运动功能。脑电图（EEG）是最常用于评估神经元活动的一种。在许多计算机视觉应用中，深度神经网络 (DNN) 显示出显着的优势。为了最终使用 DNN，我们在这里展示了一个浅层神经网络，它主要使用两个卷积神经网络 (CNN) 层，参数相对较少，并且可以快速从 EEG 中学习频谱时间特征。我们将此模型与其他三个具有不同深度的神经网络模型进行了比较，这些模型使用闭眼状态应用于心算任务，适用于患有运动障碍和视觉功能下降的患者。实验结果表明，浅层 CNN 模型优于所有其他模型，达到了 90.68% 的最高分类准确率。处理跨学科分类问题也更加稳健：准确率只有 3% 的标准偏差，而不是传统方法的 15.6%。