We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG. Instead of utilizing raw EEG channel data, we compute the joint variability of the channels in the form of a covariance matrix that provide spatio-temporal representations of EEG. The networks are trained hierarchically and the extracted features are passed onto the next network hierarchy until the final classification. Using a publicly available EEG based speech imagery database we demonstrate around 23.45% improvement of accuracy over the baseline method. Our approach demonstrates the promise of a mixed DNN approach for complex spatialtemporal classification problems.
The prior works of computation of spiculation, lobulation, and sphericity require a set of four ground truths from radiologists and, hence, can not be used in practice. The proposed methods do not require ground truth information of nodules from radiologists, and hence, it can be used in real-life computer-aided diagnosis system for lung cancer.
Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for people with speaking disabilities. As a step towards full decoding of imagined speech from active thoughts, we present a BCI system for subject-independent classification of phonological categories exploiting a novel deep learning based hierarchical feature extraction scheme. To better capture the complex representation of high-dimensional electroencephalography (EEG) data, we compute the joint variability of EEG electrodes into a channel cross-covariance matrix. We then extract the spatio-temporal information encoded within the matrix using a mixed deep neural network strategy. Our model framework is composed of a convolutional neural network (CNN), a long-short term network (LSTM), and a deep autoencoder. We train the individual networks hierarchically, feeding their combined outputs in a final gradient boosting classification step. Our best models achieve an average accuracy of 77.9% across five different binary classification tasks, providing a significant 22.5% improvement over previous methods. As we also show visually, our work demonstrates that the speech imagery EEG possesses significant discriminative information about the intended articulatory movements responsible for natural speech synthesis.
A detailed analysis of the periodic and chaotic aspects of the coupled set of nonlinear equations deduced by Sten£o is done. It is observed that period doubling takes place with increase of one of the parameters in the equation. The He¨non method is used to search for the periodic orbits,which is supported by an analysis of the power spectrum. The chaotic region is exhibited with the help of a Poincare¨map, a power spectrum and its Lyapunov exponent. From the values of the Lyapunov exponents the fractal dimension of the chaotic system is estimated.
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