Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:52-55. doi: 10.1109/EMBC48229.2022.9870887.
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.
近年来，神经科学家对脑机接口 (BCI) 设备的发展产生了兴趣。运动障碍患者可以通过 BCI 作为一种交流手段和恢复运动功能的手段从中受益。脑电图 (EEG) 是评估神经元活动的最常用方法之一。在许多计算机视觉应用中，深度神经网络 (DNN) 显示出显著的优势。为了最终使用 DNN，我们在这里提出了一种浅层神经网络，该网络主要使用两个卷积神经网络 (CNN) 层，具有相对较少的参数，并且可以快速从 EEG 中学习频谱-时间特征。我们将该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，这些模型应用于闭眼状态下的算术任务，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 模型的性能优于所有其他模型，分类准确率达到 90.68%。它还更能抵抗跨主体分类问题：仅为准确性的 3%标准偏差，而不是传统方法的 15.6%。
