DGAFF：用于EEG生物信号通道选择的深度遗传算法适应度形成
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
摘要：
脑机接口系统旨在通过将脑信号直接翻译给计算机来促进人机交互。最近，使用许多电极已经在这些系统中产生了更好的性能。然而，除了记录过程的不期望的复杂性之外，增加记录电极的数量还导致额外的时间、硬件和计算成本。信道选择技术可以在降低噪声影响的同时，降低数据维数，消除不相关信道。此外，该技术降低了实时应用中的时间和计算成本。我们提出了一种通道选择方法，它结合了顺序搜索方法与遗传算法称为深度GA适应度形成（DGAFF）。该方法加快了遗传算法的收敛速度，提高了系统的性能。系统评估基于轻量级深度神经网络，可自动执行整个模型训练过程。所提出的方法优于其他通道选择方法在分类所利用的数据集上的运动想象。
英文摘要：
Brain-computer interface systems aim to facilitate human-computer interactions in a great deal by direct translation of brain signals for computers. Recently, using many electrodes has caused better performance in these systems. However, increasing the number of recorded electrodes leads to additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection has been utilized to decrease data dimension and eliminate irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in real-time applications. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep GA Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. The proposed method outperforms other channel selection methods in classifying motor imagery on the utilized dataset.
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