531 research outputs found

    Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding

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    International audienceNeural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for analyzing such data is hence to extract such 'shift-invariant' atoms. Even though some success has been reported with existing algorithms, they are limited in applicability due to their heuristic nature. Moreover, they are often vulnerable to artifacts and impulsive noise, which are typically present in raw neural recordings. In this study, we address these issues and propose a novel probabilistic convolutional sparse coding (CSC) model for learning shift-invariant atoms from raw neural signals containing potentially severe artifacts. In the core of our model, which we call αCSC, lies a family of heavy-tailed distributions called α-stable distributions. We develop a novel, computationally efficient Monte Carlo expectation-maximization algorithm for inference. The maximization step boils down to a weighted CSC problem, for which we develop a computationally efficient optimization algorithm. Our results show that the proposed algorithm achieves state-of-the-art convergence speeds. Besides, αCSC is significantly more robust to artifacts when compared to three competing algorithms: it can extract spike bursts, oscillations, and even reveal more subtle phenomena such as cross-frequency coupling when applied to noisy neural time series

    27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

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    Sleep Stage Classification: A Deep Learning Approach

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    Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity
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