On the potential of EEG for biometrics: combining power spectral density with a statistical test (original) (raw)
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A review on different classification techniques used in EEG based biometric system
2021
Electroencephalogram (EEG) signals are recorded from brain electrical activity along the scalp, which measures voltage fluctuations resulting from ionic currents within the brain. EEG signals contains various information. EEG signals can be used in various applications, one of them is biometric authentication. Various studies show that EEG signals can act as good biometric parameter. For any biometric system first step is to process that signal, extract useful information as features from them. After that classification of the signal is done. Choice of classifier is a very important factor for determining the performance of EEG based biometric system. This paper presents review of recently published research focusing on various classification techniques used for EEG biometrics-based systems. K-nearest neighbour (k-NN), Elman neural network (ENN), Linear Discriminant Analysis (LDA), Support vector machine (SVM), Principal component analysis (PCA), Linear vector quantization (LVQ), BP...
Analysis of factors that influence the performance of biometric systems based on EEG signals
Expert Systems with Applications, 2021
Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional biometrics. The objective of this study is to analyze the factors that influence the performance of a biometric system based on electroencephalogram signals. This work uses six different classifiers to compare several decomposition levels of the discrete wavelet transform as a preprocessing technique and also explores the importance of the recording time. These classifiers are Gaussian Naïve Bayes Classifier, K-Nearest Neighbors, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron. This work proves that the decomposition level does not have a high impact on the overall result of the system. On the other hand, the recording time of electroencephalograms has a significant impact on the performance of the classifiers. It is worth mentioning that this study used two different datasets to validate the results. Finally, our experiments show that Support Vector Machine and AdaBoost are the best classifiers for this particular problem since they achieved a sensitivity, specificity, and accuracy of 85.94 ± 1.8, 99.55 ± 0.06, 99.12 ± 0.11 and 95.54 ± 0.53, 99.91 ± 0.01, and 99.83 ± 0.02 respectively.
Power Spectral Density Analysis for Human EEG- based Biometric Identification
Authentication is most important for security. There are many different systems for recognizing the person. The traditional authentication systems such as passwords have drawbacks. It is easy to be stolen. Biometric authentication systems provide the best security. However, a current technique that widely used for identification which is fingerprint has its own disadvantages. Furthermore, current techniques such as facial recognition, iris recognition and voice recognition that used to recognize person still compromise the security walls. In this recent years, electroencephalograph (EEG) signal has been discovered that it has the potential to become one of the biometric authentication systems. It is brain activities for a human. It is unique due to the EEG signal is different from person to person. In this paper, power spectral density analysis was used to analyse the electroencephalography (EEG) signal. Knearest neighbor classifier was used for classification in this paper. The accuracy results of alpha (8 -13 Hz), beta (13-30 Hz), combined alpha and beta (8 -30 Hz) and combined theta, alpha, beta and gamma (4 -40 Hz) frequency bands were compared. Overall, the percentage of accuracy was above 80%. The most suitable frequency bands for human EEG-based biometric identification in this experiment was the combined theta, alpha, beta, and gamma (4 -40 Hz). The percentage of accuracy for this frequency band was the highest among the others which is 89.21%.
EEG Analysis in Biometric Identification and Authentication
2018
This article demonstrates that EEG (Electroencephalogram) signals can be used as a biometric identifier to authenticate the person. The patterns of brainwave signals are unique for every individual and it is very difficult to use these patterns for person identification and authentication purpose. Although the brainwaves signals are much complicated to study, there are very little studies have been done on the brain wave signals based biometrics. The biometric based on these physiological traits are resistant to frauds in sensitive applications domains. In confidential areas like military organizations, government agencies secrete potential domains and likewise highly restricted areas, the biometric facility based on EEG signals can be very useful. This article represents the strategy behind implementation of EEG based biometric. Here we have achieved success in authenticating the person with the help of wavelet transform. We used 5 channels of EEG waveform as features to authentica...
Biometric Identification using Electroencephalography
2020
In this paper, investigate the use of brain activity for person identification. A biometric system is a technological system that uses information about a person. Research on brain signals show that each individual has a unique brain wave pattern. Electroencephalography signals generated by mental tasks are acquired to extract the distinctive brain signature of an individual. Electroencephalography signals during four biometric tasks, namely relax, math, read and spell was acquired from 50 subjects. Features are derived from power spectral density. Classification is performed using Feed forward neural network and Recurrent neural network. The performance of the neural model was evaluated in terms of training, performance and classification accuracies. The results confirmed that the proposed scheme has potential in classifying the EEG signals. RNN is considerably better with an average accuracy of 95% for the spell task and 92% for the read tasks in comparison with a feed forward neu...
A Review on Development of an EEG-based Biometric Authentication System
2018
The authentication system is the system that provides security and ensures confidentiality of information. Biometric systems provide the best security among other authentication systems. There are some classical approaches of biometric systems for authentication such as fingerprints, eyeballs and voices. However, the threat of fake fingerprints, eyeballs and recorded voices still compromise security walls. Electroencephalogram (EEG) signal is the electrical activity of the brain which may contain much useful information. It can possibly be used for building a robust biometric recognition because of its uniqueness. EEG signal is required to undergo advanced signal processing in order to get the useful information. There are three main stages included in advanced signal processing namely preprocessing, feature extraction and classification. The techniques of signal processing are categorized into two groups which are linear and non-linear. Although many different research activities h...
Use of EEG as a Unique Human Biometric Trait for Authentication of an Individual
2020
With the advancement of biomedical technology, human brain signals are easy to measure and which are known as electroencephalogram (EEG) signals. These signals are used in different applications. One of the applications for brain waves is biometric authentication. For any signal to use as biometric parameter, it must possess some biometric characteristics such as universality, uniqueness, permanence, collectability, performance, acceptance, and circumvention. EEG has several characteristic to use as biometric parameter. This paper shows the uniqueness of EEG signal using some statistical parameters that support the uniqueness property of EEG.
EEG based biometric framework for automatic identity verification
The Journal of VLSI Signal Processing …, 2007
The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies-Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56T1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud.
Combination of multiple detectors for EEG based biometric identification/authentication
2012 IEEE International Carnahan Conference on Security Technology (ICCST), 2012
The different structures of the brain of human beings produce spontaneous electroencephalographic (EEG) records that can be used to identify subjects. This paper presents a method for biometric authorization and identification based on EEG signals. The hardware uses a simple 2-signal electrode and a reference electrode configuration. The electrodes are positioned in such a way to be as unobtrusive as possible for the tested subject. Multiple features are extracted from the EEG signals that are processed by different classifiers. The system uses all the possible combinations between classifiers and features, fusing the best results. The fused decision improves the classification performance for even a small number of observation vectors. Results were obtained from a population of 50 subjects and 20 intruders, both in authentication and identification tasks. The system obtains an Equal Error Rate (EER) of 2.4% with only a few seconds for testing. The obtained performance measures are an improvement over the results of current EEG-based systems.
2020
EEG (electroencephalogram) based biometrics systems are used in very high-security areas due to its several advantages over traditional biometric systems. This paper presents an approach for extracting features and classification of EEG signals acquired from users for authentication purposes. The Autoregressive (AR) model with order three features is calculated because the AR model features reveal the signal's intrinsic characteristics. An experiment is performed on many classifiers to classify the extracted features. Classifiers are tested with different kernels and optimizers to accomplish good accuracy for the system. Machine learning algorithms such as support vector machines (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP), XGBoost are used as classifiers to classify the signals for authentication. Cross-validation is used for splitting data in the train and test set so that more accurate results were obtained on unseen data. 10-fold cross-validation is used in...