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

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 AR 20 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.

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

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Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

“…Because, it can provide mainly three types of benefits such as (i) reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant, (ii) helps to improve the performance, and (iii) reduce the setup time in some applications [26]. Moreover, the channel selection approach was used as an effective tool by many researchers in different fields, such as EEG emotion [26]- [30], personal identification [31], [32], user identification [33], seizure detection [34], [41], intruder detection [35], screening of alcoholism [36], depression detection [39], [40], detecting drowsiness [42], auditory attention detection [37], [38], brain-computer interfaces [43], [44] and so on. It was noted that several studies proposed effective predictive-based approaches for the detection of children with ADHD without selecting potential channels from EEG signals [4], [12]- [14], [24], [25], [45]- [48].…”

EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

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