EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based ap...
A promising approach to overcome the various shortcomings of password systems is the use of biometric authentication, in particular the use of electroencephalogram (EEG) data. In this paper, we propose a subject-independent learning method for EEG-ba...
The increasing integration of the Internet of Things (IoT) into daily life has led to significant changes in our social interactions. The advent of innovative IoT solutions, combined with the enhanced capabilities and expanded reach of 5G wireless ne...
In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity informatio...
This overview examines recent advancements in EEG-based biometric identification, focusing on integrating emotional recognition to enhance the robustness and accuracy of biometric systems. By leveraging the unique physiological properties of EEG sign...
Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram...
Using biometric modalities for person recognition is crucial to guard against impostor attacks. Commonly used biometric modalities, such as fingerprint scanners and facial recognition, are effective but can easily be tampered with and deceived. These...
The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentic...
Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns. This study proposes a framework based on a convolutional autoencoder (CAE) along with a traditio...
Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and cri...
With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteris...
The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EE...
Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily....
This study investigates the use of electroencephalography (EEG) signals for user authentication as an innovative approach to enhancing security within the cybersecurity domain. Motivated by the limitations of traditional authentication mechanisms, we...
The rapid advancement of artificial intelligence (AI) has spurred innovation across various domains—information technology, medicine, education, and the social sciences—and is likewise creating new opportunities in architecture for unders...
A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and in...
The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of...
Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authenticatio...
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be a...
Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promisi...
The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identifi...
Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to p...
A new authentication method based on EEG signal is proposed here. Biometric features such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even brainwave patterns can be used for authentication methods. Brainwave pat...
Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive a...
Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have...
A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving us...
Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The deve...
With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured s...
Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unl...
Person authentication, based on electroencephalography (EEG) signals, is one of the directions possible in the study of EEG signals. In this paper, a method for the selection of EEG electrodes and features in a discriminative manner is proposed. Give...
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based...
Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfusc...
This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), whic...
In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human–computer interaction), etc. Such emotions could be detected from various means, such as information i...
The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant...
Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promis...
In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate o...
Individual brain activity patterns derived from electroencephalogram (EEG) data offer a unique source for personal identification, introducing a novel approach to the field. Autoencoders are well-known machine learning models that automate feature ex...
Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is hig...
Internet addiction (IA), as a new and often unrecognized psychosocial disorder, endangers people’s health and their lives. However, the common biometric analysis based on the combination of EEG signals and results of questionnaires is not quant...
Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in...
The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable...
Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012&...