An Efficient Score level Multimodal Biometric System using ECG and Fingerprint

Biometric system is a security system that uses human’s unique traits to identify and authenticate the user. Biometrics refers to biological traits of a human that are often categorized as physiological traits like fingerprint, iris, face and behavioral characteristics like signature style, voice and typing rhythm. The Biological signals like Electrocardiography (ECG), Electromyography(EMG), and Electroencephalography (EEG) have not been explored to biometric applications as their scope was limited to medical applications only. Recent survey suggests that these biological signals can be explored as a part of the biometric application. The main objective of this paper is to explore the possibility of using the ECG as a part of multimodal biometric. ECG has lower accuracy but fusing it with a traditional biometric like fingerprint yields a higher accuracy rate and it is really difficult to spoof the system. The proposed multimodal biometrics system has an accuracy of 98% with the false acceptance rate of 2% and almost 0% of false rejection rate

Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method

Signal Processing and Classification of Visual Evoked Potentials in a Brain Computer Interface

This research thesis explores an efficient communication approach in P300 based single trial brain-computer interface (BCI). As a tool of rehabilitation engineering for the locked-in patients, the BCI is expected to be swift in performance and intelligent in recognition. With this aim, the objectives of this thesis are to reduce noise from raw EEG signals using novel variants of principal component analysis (PCA) and singular value decomposition (SVD), to improve classification performance using Fuzzy ARTMAP, Simplified Fuzzy ARTMAP and a combination of other linear classifier and to reduce feature dimension and hardware requirement using genetic algorithm (GA)

Extracting Single Trial Visual Evoked Potentials using Selective Eigen-Rate Principal Components

In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli

Whitening of Background Brain Activity via Parametric Modeling

Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities of two parametric techniques: a direct one based on Levinson solution of Yule-Walker equations, called AR Yule-Walker, and an indirect one based on the least-squares solution of forward-backward linear prediction ( FBLP) equations, called AR-FBLP. The whitening effect of the two algorithms is investigated with real background electroencephalogram ( EEG) colored noise and compared in time and frequency domains. Copyright (C) 2007

Classification of motor imaginary tasks using adaptive recursive bandpass filter - Effective classification for motor imaginary BCI

The noteworthy point in the advancement of Brain Computer Interface (BCI) research is not only to develop a new technology but also to adopt the easiest procedures since the expected beneficiaries are of disabled. The nature of the locked-in patients is that, they possess strong mental ability in thinking and understanding but they are extremely unable to express their views. Imagination is possible for almost all of the locked-in patients; hence a BCI which does not rely on finger movements or other muscle activity is definitely an added advantage in this arena. The objective of this paper is to identify and classify motor imaginary signals extracted from the left and right cortex of the human brain. This is realised by implementing an adaptive bandpass filter with the combination of frequency shifting and segmentation techniques. The signals are captured using Electro-Encephalogram (EEG) from the C3, C4, and Cz channels of the scalp electrodes and is pre-processed to expose the motor imaginary signals. The result of classification using a simple threshold articulates the effectiveness of our proposed technique. The best results were found in the latency range of 3 to 9 seconds of the imagination and this proves the existing neuro-science knowledge