Kenneth Revett

EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

A Survey of User Authentication Based on Mouse Dynamics

This work surveys biometric based authentication systems that deploy mouse movements. Typically, timing and movement direction, along with clicking actions are used to build a profile of a user, which is then used for authentication purposes. Most system relies on a continuous monitoring process, or require the user to interact with a program (such as a game) in order to derive sufficient statistical information regarding their mouse dynamics. In this work, a novel graphical authentication system dubbed Mouse-lock is presented. This system deploys the analogy of a safe, and the password is entered via the mouse in a graphical equivalent of combination lock. The question is whether this approach elicits sufficient discriminatory information from a relatively minimalist degree of interaction from the user. The preliminary results from a study with six subjects indicates, based on FAR/FRR values, that this is a viable approach.

Password secured sites - stepping forward with keystroke dynamics

Computer authentication is a critical component of most computer systems -especially those used in e-commerce activities over the Internet. Global access to information makes security, namely the authentication process, a critical design issue in these systems. In what concerns to authentication, what is required is a reliable, hardware independent and efficient security system. In this paper, we propose an extension to a keystroke dynamics based security system. We provide evidence that completely software based systems can be as effective as expensive and cumbersome hardware based systems. Our system is a behavioral based system that captures the normal typing patterns of a user and uses that information, in addition to standard login/password security to provide a system that is user friendly and very effective at detecting imposters. The results provide a means of dealing with enhanced security that is growing in demand in Web based applications based on e-commerce.

A machine learning approach to keystroke dynamics based user authentication

The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the use of a behavioural biometric based on keystroke dynamics. Although there are several implementations of keystroke dynamics available, their effectiveness is variable and dependent on the data sample and its acquisition methodology. The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed. Our results also provide evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.

A Breast Cancer Diagnosis System: A Combined Approach Using Rough Sets and Probabilistic Neural Networks

In this paper, we present a medical decision support system based on a hybrid approach utilizing rough sets and a probabilistic neural network. We utilized the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilized a probabilistic neural network to perform supervised classification. Our results indicate that rough sets were able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%

Biosignals for User Authentication - Towards Cognitive Biometrics?

Cognitive biometrics refers to a novel approach for user authentication/identification utilising biosignals which reflect the mental and emotional states of an individual. Specifically, current implementations rely on the use of the electroencephalogram (EEG), electrocardiogram (ECG), and the electro dermal response (EDR) as inputs into a traditional authentication scheme. The motivation for the deployment of biosignals resides in their potential uniqueness, universality, and their resistance to spoofing. The challenge with respect to cognitive biometrics based on biosignals is to enhance the information content of the acquired data. This paper presents a brief survey of the use of such biosignals to produce cognitive biometric systems for person recognition. The types of signals used and their claimed effectiveness is presented and compared. The paper concludes with a description of the challenges facing the deployment of cognitive biometrics, including sensor design issues and the need to extract information-rich and robust features.

Automatic landmarking of 2 d medical shapes using the growing neural gas network

MR Imaging techniques provide a non-invasive and accurate method for determining the ultra-structural features of human anatomy. In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. Our approach is based on an automated landmark extraction algorithm which automatically selects points along the contour of the ventricles from a series of 2D MRI brain images. Automated landmark extraction is accomplished through the use of the self-organising network the growing neural gas (GNG) which is able to topographically map the low dimension of the network to the high dimension of the manifold of the contour without requiring a priori knowledge of the structure of the input space. The GNG method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and an error metric is applied to quantify the performance of our algorithm compared to the other two.

Data Mining a Keystroke Dynamics Based Biometrics Database Using Rough Sets

Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not

Cognitive Biometrics: Challenges for the Future

Cognitive biometrics is a novel approach to user authentication/identification which utilises a biosignal based approach. Specifically, current implementations rely on the use of the electroencephalogram (EEG), electrocardiogram (ECG), and the electrodermal response (EDR) as inputs into a traditional authentication scheme. The scientific basis for the deployment of biosignals resides principally on their uniqueness -for instance the theta power band in adults presents a phenotypic/genetic correlation of approximately 75%. The numbers are roughly the same for ECG, with an heritability correlation for the peak-to-peak (R-R interval) times of over 77%. For EDR, the results indicate that there is approximately a 50% heritability score (h2). The challenge with respect to cognitive biometrics based on biosignals is to enhance the information content of the acquired data.