Person identification using autoencoder-CNN approach with multitask-based EEG biometric

作     者：Bandana Das, Banee Kumar Ram, Saswat Sathya Babu, Korra Mohapatra, Ramesh Kumar Mohanty, Saraju P. 

作者机构：SRM Univ Comp Sci & Engn Amravati Andhra Pradesh India SRM Univ Dept Elect & Commun Engn Amravati Andhra Pradesh India IIITDM Comp Sci Engn Kurnool India Natl Inst Technol Rourkela Comp Sci & Engn Rourkela India Univ North Texas Comp Sci & Engn Denton TX USA 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (Multimedia Tools Appl)

年 卷 期：2024年第83卷第35期

页      面：83205页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Electroencephalography (EEG) Biometric authentication Person identification Autoencoder Convolutional neural network 

摘      要：In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognize, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject s motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available motor imagery EEG dataset with 109 subjects. The highest recognition rate of 87.60% for task-based identification and 99.89% recognition rate for resting-state has been recorded using the Autoencoder-CNN model. The outcomes imply that the overall performance of our proposed framework is similar or advanced to that of the state-of-the-art method. The shape is a realistic technique to control the full-size extent of EEG data and to pick out the individual based totally on their specific task.