EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

High level security has nurtured the arisen of Electroencephalograms (EEG) signals as a noteworthy biometrics modality for person authentication modelling. Modelling distinctive characteristics among individuals, especially in a dynamic environment involves incremental knowledge updates from time to time. K-Nearest Neighbour (KNN) is a well-known incremental learning method which applies First-In-First-Out (FIFO) knowledge update strategy. However, it is not suitable for person authentication modelling because it cannot preserve the representative EEG signals patterns when individual characteristics changes over time. Fuzzy-Rough Nearest Neighbours (FRNN) technique is an outstanding technique to model uncertainty under an imperfect data condition. The current implementation of FRNN technique is not designed for incremental learning problem because there is no update function to incrementally reshape and reform the existing knowledge granules. Thus, this research aims to design an Incremental FRNN (IncFRNN) technique for person authentication modelling using feature extracted EEG signals from VEP electrodes. The IncFRNN algorithm updates the training set by employing a heuristic update method to maintain representative objects and eliminate rarely used objects. The IncFRNN algorithm is able to control the size of training pool using predefined window size threshold. EEG signals such as visual evoked potential (VEP) is unique but highly uncertain and difficult to process.There exists no consistant agreement on suitable feature extraction methods and VEP electrodes in the past literature. The experimental comparison in this research has suggested eight significant electrodes set located at the occipital area. Similarly, six feature extraction methods, i.e. Wavelet Packet Decomposition (WPD), mean of amplitude, coherence, crosscorrelation, hjorth parameter and mutual information were used construct the proposed person authentication model. The correlation-based feature selection (CFS) method was used to select representative WPD vector subset to eliminate redundancy before combining with other features. The electrodes, feature extraction, and feature selection analysis were tested using the benchmarking dataset from UCI repositories. The IncFRNN technique was evaluated using a collected EEG data from 37 subjects. The recorded datasets were designed in three different conditions of ambient noise influence to evaluate the performance of the proposed solution. The proposed IncFRNN technique was compared with its predecessor, the FRNN and IBk technique. Accuracy and area under ROC curve (AUC) were used to measure the authentication performance. The IncFRNN technique has achieved promising results. The results have been further validated and proven significant statistically using paired sample ttest and Wilcoxon sign-ranked test. The heuristic incremental update is able to preserve the core set of individual biometrics characteristics through representative EEG signals patterns in person authentication modelling. Future work should focus on the noise management in data acquisition and modelling process to improve the robustness of the proposed person authentication model

Distraction descriptor for brainprint authentication modelling using probability-based incremental fuzzy-rough nearest neighbour technique

The characteristics of uniqueness and proof of aliveness have driven the research in Brainprint as a biometric modality. Brainprint measuring by Electroencephalogram (EEG) suffers from low signal-to-noise ratio and are varied across time. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real- world situations. Thus, making use of the distraction is wiser than eliminating it. This research aims to design a distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based update strategy in Incremental Fuzzy-Rough Nearest Neighbor (IncFRNN) technique. The research follows the experimental methodology, starting from data acquisition to data imputation, EEG distraction descriptor, probability-based IncFRNN and model analysis. The EEG of 45 volunteer human subjects were collected using visual stimuli in three levels of auditory ambient distraction, which are in quiet, low, and high distraction conditions. An artefact rejection with amplitude greater than 100 µV was applied for data cleaning. Occasionally, missing values occurred after removing the noisy trials. A similarity matching imputation method is proposed for EEG data imputation. The power spectral density, wavelet phase stability, and coherence were used as feature extraction methods. The probability-based IncFRNN technique was used to construct the learning model. The proposed probability- based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First- In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The authentication accuracy, area under receiver operating characteristic curve, recall, precision, and the F-measure were used to evaluate the proposed technique. The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in the uncontrolled environment. However, the authentication results in low distraction condition are significantly worse than both the quiet and high distraction conditions. This might because the distraction is too mild to elicit the cognitive measures representing individual characteristics. The probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the data acquisition was carried out in a single session. The EEG distraction descriptor may vary due to intersession variability. Future research should focus on the intersession variability to improve the robustness of the brainprint authentication model

Person authentication using electroencephalogram (EEG) brainwaves signals

This chapter starts with the introduction to various types of authentication modalities, before discussing on the implementation of electroencephalogram (EEG) signals for person authentication task in more details. In general, the EEG signals are unique but highly uncertain, noisy, and difficult to analyze. Event-related potentials, such as visual-evoked potentials, are commonly used in the person authentication literature work. The occipital area of the brain anatomy shows good response to the visual stimulus. Hence, a set of eight selected EEG channels located at the occipital area were used for model training. Besides, feature extraction methods, i.e., the WPD, Hjorth parameter, coherence, cross-correlation, mutual information, and mean of amplitude have been proven to be good in extracting relevant information from the EEG signals. Nevertheless, different features demonstrate varied performance on distinct subjects. Thus, the Correlation-based Feature Selection method was used to select the significant features subset to enhance the authentication performance. Finally, the Fuzzy-Rough Nearest Neighbor classifier was proposed for authentication model building. The experimental results showed that the proposed solution is able to discriminate imposter from target subjects in the person authentication task

EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model

A Comparative Work of Incremental Learning and Ensemble Learning for Brainprint Identification

Electroencephalogram (EEG) signals are nonstationary and vary across time. The static learning model requires large training data to ensure sufficient knowledge acquisition to build a robust model. However, it is very challenging to achieve complete concept learning due to the behavioural changes in model learning. This issue is particularly critical in brainprint identification, where data acquisition in a short time cannot ensure sufficient training data for comprehensive model learning. Thus, dynamic learning, i.e., incremental learning and ensemble learning, presents a better solution for encapsulating EEG signal changes and variations. Both incremental and ensemble learning follow different approaches to manage the concept learning. Incremental learning merges new variations of EEG signals into the existing learning model over time, while ensemble learning uses multiple models for prediction. Nevertheless, limited research works were reported on comparing these two learning methods to prove the efficiency in handling nonstationary data for brainprint identification. Thus, this paper aims to compare incremental learning and ensemble learning for brainprint identification modelling. Incremental Fuzzy-Rough nearest Neighbour (IncFRNN) and Random Forest are selected to represent incremental learning and ensemble learning, respectively. Accuracy, area under the ROC curve (AUC) and F-measure were used to evaluate the classification performance. The experimental results proved that incremental learning outperformed ensemble learning when the training data were limited. The classification results of IncFRNN model were recorded at 0.9160, 0.9827 and 0.9169 while the Random Forest model only yielded 0.8113, 0.9709, and 0.9169 in accuracy, AUC, and F-measure, respectively. The ongoing learning process in incremental learning helps to capture the new changes in EEG signals and improve the classification performance

EEG-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometricauthentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. Theembedded heuristic update method adjusts the knowledge granules incrementally to maintain all representativeelectroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granulesthrough insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reducethe overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processingsteps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. Theexperimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNNtechnique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured interms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. Theproposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window sizeenvironment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model

Wavelet Feature Extraction And J48 Decision Tree Classification Of Auditory Late Response (ALR) Elicited By Transcranial Magnetic Stimulation

Nowadays, transcranial magnetic stimulation (TMS) has been used to treat major depression and migraine. Integrating transcranial magnetic stimulation and electroencephalogram (TMS - EEG) may provide beneficial information. This paper introduces the experimental design, experimental setup and experimental procedures to differentiate the repetitive transcranial magnetic stimulation (rTMS) and without TMS over N100 (N1) and P200 (P2) peaks with regards to auditory attention. New experimental design, setup and procedures are developed to elicit N1 and P2 through the recording of EEG signal with the excitation of neurons from TMS and pure tones. Wavelet transform is implemented as feature extraction for the selected data. Four features are used for the classification. The classification is based on J48 decision tree performed using WEKA to distinguish between without TMS and rTMS. The result between without TMS and rTMS (in attention condition) showed 98.85% accuracy meanwhile between without TMS and rTMS (no attention condition) showed 99.46% accuracy

Data Imputation in EEG Signals for Brainprint Identification

Electroencephalograms (EEG) signals have very low signal-to-noise ratio, thus can be easily affected and changes over milliseconds. Normally, trials with excessive body movements or other types of artefacts with amplitude more than 100 µV should be discarded to reduce the noise stains. Scrapping the affected features is not advisable. Therefore, missing values imputation is essential to avoid incomplete data that may deteriorate the computational modelling performance. Hence, this paper proposes a similarity matching method to replace the missing values in the EEG trials. The main idea of the missing values imputation is based on the similarity measure between the trials. The trials with the highest similarity is taken to replace the missing values for the related EEG channels. Statistical evaluation and classification evaluation are used to evaluate the reliability of the proposed similarity matching method. The mean, variance and standard deviation are used for statistical evaluation. For the classification evaluation, the dataset is classified for brainprint identification by using the Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN). The statistical evaluation proved that the proposed similarity matching imputation method is promising when the missing values are not come from the same channels. The classification results achieved the excellent performance with 98.19% in accuracy and 0.998 in AUC

Comparing Features Extraction Methods for Person Authentication Using EEG Signals

This chapter presents a comparison and analysis of six feature extraction methods which were often cited in the literature, namely wavelet packet decomposition (WPD), Hjorth parameter, mean, coherence, cross-correlation and mutual information for the purpose of person authentication using EEG signals. The experimental dataset consists of a selection of 5 lateral and 5 midline EEG channels extracted from the raw data published in UCI repository. The experiments were designed to assess the capability of the feature extraction methods in authenticating different users. Besides, the correlation-based feature selection (CFS) method was also proposed to identify the significant feature subset and enhance the authentication performance of the features vector. The performance measurement was based on the accuracy and area under ROC curve (AUC) values using the fuzzy-rough nearest neighbour (FRNN) classifier proposed previously in our earlier work. The results show that all the six feature extraction methods are promising. However, WPD will induce large vector set when the selected EEG channels increases. Thus, the feature selection process is important to reduce the features set before combining the significant features with the other small feature vectors set

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London