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|dc.description.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). � 2018 Elsevier B.V.||en_US|
|dc.title||A pervasive electroencephalography-based person authentication system for cloud environment||en_US|
|Appears in Collections:||Research Publications|
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