Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.

中文翻译：

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用于基于EEG的跨任务识别的深度描述符

由于生命体征在专家系统中的应用，出现了新的方法，生命体征已经在生物识别技术中获得了发展。这些信号之一是脑电图（EEG）。受试者正在执行甚至思考的运动任务会影响脑电波的模式并干扰所获取的信号。在这项工作中，从跨任务的角度探讨了具有EEG信号的生物识别技术。基于深层卷积网络（CNN）和挤压和激励块，开发了一种新颖的方法来产生深层脑电信号描述符，以评估脑电信号中的运动任务对生物特征验证的影响。Physionet脑电图运动数据/图像数据集在此处用于方法评估，该数据集具有109个执行不同任务的受试者的64个EEG通道。由于数据集提供的数据量不足以有效地训练Deep CNN模型，因此还提出了一种数据增强技术以实现更好的性能。提出了一种评估协议，以评估有关EEG通道数量的鲁棒性，并强制训练和测试集，而不会出现个人重叠。跨任务方案（EER为0.1％）获得了最新的技术成果，基于挤压和激励的网络在四个交叉个人方案中的三个克服了简单的CNN体​​系结构。提出了一种评估协议，以评估有关EEG通道数量的鲁棒性，并强制训练和测试集，而不会出现个人重叠。跨任务方案（EER为0.1％）获得了最新的技术成果，基于挤压和激励的网络在四个交叉个人方案中的三个克服了简单的CNN体​​系结构。提出了一种评估协议，以评估有关EEG通道数量的鲁棒性，并强制训练和测试集，而不会出现个人重叠。跨任务方案（EER为0.1％）获得了最新的技术成果，基于挤压和激励的网络在四个交叉个人方案中的三个克服了简单的CNN体​​系结构。