Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain.
Department of Communications Engineering, University of Málaga, 29071 Málaga, Spain.
Sensors (Basel). 2021 Mar 17;21(6):2096. doi: 10.3390/s21062096.
Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models.
脑电图（EEG）信号分类是一项具有挑战性的任务，因为信号的信噪比通常较低，并且通常存在来自不同来源的伪影。以前已经提出了不同的分类技术，这些技术通常基于从 EEG 频带功率分布轮廓中提取的预定义特征集。然而，取决于实验条件和要捕获的响应，EEG 的分类仍然具有挑战性。在这种情况下，使用深度神经网络为无需使用预定义特征集即可提高分类性能提供了新的机会。然而，深度学习体系结构包括大量的超参数，模型的性能依赖于这些超参数。在本文中，我们提出了一种优化深度学习模型的方法，不仅优化超参数，还优化其结构，该方法能够提出由于不同层组合而由不同架构组成的解决方案。实验结果证实，我们的方法优化的深度架构优于基准方法，并生成计算效率高的模型。此外，我们证明优化的架构可以提高相对于基准模型的能量效率。
