Abstract Cloud security is a major concern to the current research community. Existing solutions are vulnerable to various threats and can easily be forged. Electroencephalography (EEG) signals can provide unique identification that is being explored by various research groups for the development of biometric solutions. In this paper, we present a person authentication framework suitable for cloud environment using EEG signals. EEG signals are recorded in a mobile device while the participants listen to music. Recorded signals are then transferred to a cloud server using Representational State Transfer (REST) web service, where useful features are extracted. Person identification and verification processes are done using two well known classifiers, namely Hidden Markov Model (HMM) and Support Vector Machine (SVM). We have improved the performance of the system using a decision fusion approach. A total of 40 volunteers have participated in this study for collecting brain activity data. Identification accuracy has been recorded as 97.5%. Effectiveness of the proposed framework has been validated using Receiver Operating Characteristic (ROC) curve with 100% True Positive Rate (TPR) and 35% False Acceptance Rate (FAR).

中文翻译：

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一种基于普适脑电图的云环境人员认证系统

摘要 云安全是当前研究社区的主要关注点。现有的解决方案容易受到各种威胁的影响，并且很容易被伪造。脑电图 (EEG) 信号可以提供独特的识别信息，各种研究小组正在探索该信息以开发生物识别解决方案。在本文中，我们提出了一种适用于使用 EEG 信号的云环境的人员身份验证框架。当参与者听音乐时，脑电图信号被记录在移动设备中。然后使用 Representational State Transfer (REST) Web 服务将记录的信号传输到云服务器，从中提取有用的特征。人员识别和验证过程是使用两个众所周知的分类器完成的，即隐马尔可夫模型 (HMM) 和支持向量机 (SVM)。我们使用决策融合方法改进了系统的性能。共有 40 名志愿者参与了这项研究，以收集大脑活动数据。识别准确率已记录为97.5%。已使用具有 100% 真阳性率 (TPR) 和 35% 错误接受率 (FAR) 的接收者操作特征 (ROC) 曲线验证了所提议框架的有效性。