Abstract
Electroencephalogram (EEG) denotes to the brain waves whereas Electrooculogram (EOG) denotes the eye blinking signals. Both the signals are accompanied by various artifacts when they are recorded. Preprocessing becomes an important task in order to get rid of artifacts and use these signals in various biometric and clinical applications. Stationary Wavelet transform (SWT) with the combination of Independent Component Analysis (SWT + ICA) is used to preprocess EEG signal and Empirical Mode Decomposition (EMD) is used to preprocess EOG data. After the processing/filtering of both the signals, feature extraction is done. For the purpose of feature extraction, time delineation in the case of EOG and Auto-Regressive Modeling (AR) technique in the case of EEG signal is implemented. In order to minimize the number of features, fusion of extracted features is performed using Canonical Correlation Analysis (CCA). In order to perform dimensionality reduction, classification is performed which classifies the features into sets. Artificial Neural Network (ANN) is used to form suitable feature arrays and evaluate the classifier’s performance. The chief goal is to develop a multimodal system which possesses high classification and recognition accuracy so that biometric authentication can be performed using the combination of EEG and EOG signals.
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Bhateja, V., Gupta, A., Mishra, A., Mishra, A. (2019). Artificial Neural Networks Based Fusion and Classification of EEG/EOG Signals. In: Satapathy, S., Bhateja, V., Somanah, R., Yang, XS., Senkerik, R. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 863. Springer, Singapore. https://doi.org/10.1007/978-981-13-3338-5_14
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