43 research outputs found

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

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    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

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    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

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    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
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