PIN generation using EEG : a stability study

In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain–computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of this method is obviously its better fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e. to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable

A rough sets based classifier for primary biliary cirrhosis

In this paper, a decision support system is presented based on the machine learning approach of rough sets. The resulting decision support system was able to reduce the dimensionality of the data, produce a highly accurate classifier, and generate a rule based classifier that is readily understood by a domain expert. These preliminary results indicate that the rough sets machine learning approach can be successfully applied to biomedical datasets that contain a variety of attribute types, missing values and multiple decision classes

Animal toxins: what features differentiate pore blockers from gate modifiers

A surprisingly large number of animal toxins target voltage sensitive ion channels. Even though there exists toxins for all four major voltage sensitive ion channels, a majority act either on sodium or potassium channels. Given a specific primary sequence, the challenge is to determine in an automated fashion whether a given substance is toxic, and what its site of action might be. Currently, there are signals such as functional dyads that are indicative of a toxin, but are not yet specific enough to allow accurate prediction of the site of action. In this paper, an automated approach for detecting whether a toxin acts on voltage-sensitive sodium versus potassium channels is presented. In addition, our consensus sequence is also able to reliably determine whether the toxin acts as a gate modifier or pore blocker (> 93% accuracy)

Computational modelling of the gene expression profile from acute ischaemic brain injury

The ensuing events subsequent to cerebral ischaemia are complex and multi-faceted, making it difficult to extract causal relationships between the various pathways that are altered during ischaemia. In this study, we analyse a comprehensive DNA microarray dataset of acute experimental ischaemic stroke, in an effort to elucidate key regulatory elements that participate in the triggering of the pathways that lead to tissue damage. The data suggest that genes responsible for immediate early genes, apoptosis, neurotransmitter receptors (principally glutamate), and inflammation are differentially expressed at various time points subsequent to experimental ischaemia. Using unsupervised clustering (self-organising maps) and gene regulatory networks, we were able to establish a framework within which we could place the resultant gene expression changes into. Although not yet complete, the results from this study indicate that even a complicated pathology such as ischaemia can be analysed in a biologically meaningful way using DNA microarray technology

A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets

This paper compares two different rule based classification methods in order to evaluate their relative efficiacy with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS

Data mining an EEG dataset with an emphasis on dimensionality reduction

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early attempts to analyse EEG data relied on visual inspection of EEG records. Since the introduction of EEG recordings, the volume of data generated from a study involving a single patient has increased exponentially. Therefore, automation based on pattern classification techniques have been applied with considerable success. In this study, a multi-step approach for the classification of EEG signal has been adopted. We have analysed sets of EEG time series recording from healthy volunteers with open eyes and intracranial EEG recordings from patients with epilepsy during ictal (seizure) periods. In the present work, we have employed a discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time - that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. Principal components analysis (PCA) and rough sets have been used to reduce the data dimensionality. A multi-classifier scheme consists of LVQ2.1 neural networks have been developed for the classification task. The experimental results validated the proposed methodology

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