论文标题
用机器和深度学习分析脑电图数据：基准测试
Analyzing EEG Data with Machine and Deep Learning: A Benchmark
论文作者
论文摘要
如今，机器和深度学习技术被广泛用于不同领域，从经济学到生物学。通常，这些技术可以通过两种方式使用：试图将众所周知的模型和体系结构适应可用数据或设计自定义体系结构。在这两种情况下，为了加快研究过程，知道哪种类型的模型最适合特定问题和/或数据类型非常有用。通过专注于脑电图信号分析，也是文献中的第一次，在本文中，提出了机器的基准和脑电信号分类的深度学习。在我们的实验中，我们使用了四种最广泛的模型，即多层感知器，卷积神经网络，长期短期记忆和封闭式复发单元，突出显示哪个可以成为开发脑电图分类模型的良好起点。
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available data, or designing custom architectures. In both cases, to speed up the research process, it is useful to know which type of models work best for a specific problem and/or data type. By focusing on EEG signal analysis, and for the first time in literature, in this paper a benchmark of machine and deep learning for EEG signal classification is proposed. For our experiments we used the four most widespread models, i.e., multilayer perceptron, convolutional neural network, long short-term memory, and gated recurrent unit, highlighting which one can be a good starting point for developing EEG classification models.
