EEG‐based biometric authentication system using convolutional neural network for military applications

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2023-10-06 DOI:10.1002/spy2.345
Himanshu Vadher, Pal Patel, Anuja Nair, Tarjni Vyas, Shivani Desai, Lata Gohil, Sudeep Tanwar, Deepak Garg, Anupam Singh
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Abstract

Abstract In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.
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基于脑电的卷积神经网络生物识别认证系统在军事上的应用
在这个技术时代,随着安全需求的增加,生物识别技术作为一种安全便捷的人类身份识别和验证方法在身份认证系统中的应用越来越多。脑电图(EEG)信号由于其独特和不可伪造的特点,在各种可用的生物识别模式中受到了极大的关注。在这项研究中,我们提出了一种基于EEG的多主体多任务生物识别认证系统,用于军事应用,解决与EEG信号多任务变化相关的挑战。拟议的工作考虑使用各自的脑电图信号,以便只有经过认证的人员才能进入大炮、进入高度机密的军事场所等等。我们使用了一个多主题、多会话和多任务的数据集。使用基本的信号处理技术对数据集进行了部分预处理,如坏通道修复、用于去除伪影的独立分量分析、降采样至250 Hz,以及用于信号即兴处理的0.01-200 Hz音频滤波器。这个部分预处理的数据集被进一步处理,并用于我们的深度学习模型(DL)架构。对于基于脑电图的生物识别认证,卷积神经网络(CNN)优于许多最先进的深度学习架构,其验证精度约为99.86%,训练精度为98.49%,精度,召回率和F1得分为99.91%,这使得这种基于脑电图的认证方法更加可靠。还基于训练和推理时间对DL模型进行了比较,其中CNN使用了最多的训练时间,但花费了最少的时间来预测输出。我们通过输入mel谱图、色谱图和mel频率倒谱系数,比较了三种预处理技术下CNN模型的性能,其中mel谱图的效果更好。该体系结构在军事应用中具有鲁棒性和高效性。
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