EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Zaid Abdi Alkareem Alyasseri,Mazin Abed Mohammed,V Rajinikanth,Karrar Hameed Abdulkareem,Seifedine Kadry,Osama Ahmad Alomari,Seungmin Rho,Mohammed A Awadallah,Mohammed Azmi Al-Betar,Bai Yuan Ding

doi:10.1155/2022/5974634

Abstract

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Highlights

Over many years, our universe has transferred to a digital community in which each person is living with a particular digital identifier
5.1. e Performance of Traditional EEG Classification Methods. e main purpose of this section is to provide a brief idea about the performance of traditional machine learning classification approach used for EEG-based personal identification problem. e measurements used to evaluate the performance are the classification accuracy and the area under curve (AUC). e results obtained are summarized in Table 2 using three datasets
The performance of the proposed channel selection approach and other approaches was evaluated using three EEG signal datasets collected by applying autoregressive (AR) models according to three different coefficients. e solution representation in all channel selection approaches is represented by a vector that consists of a series of 1’s and 0’s, where “1” means that the channel is selected and “0” means that the channel is ignored

Summary

Introduction

Our universe has transferred to a digital community in which each person is living with a particular digital identifier. Erefore, personal behavior or characteristics can be used to strengthen identification systems. The widespread and influential deployment of biometric systems leads to a new challenge, which is called “spoofing” [1, 6,7,8]. Such type of attack is classified as the most dangerous in security systems since it is designed to break the biometric systems’ security, allowing unwarranted persons to get admission to the system [2]

Methods

Findings

Discussion

Conclusion