Signal2Image Modules in Deep Neural Networks for EEG Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201

Biometric data and machine learning methods in the diagnosis and monitoring of neurodegenerative diseases: a review

Представлен обзор неинвазивных биометрических методов выявления и прогнозирования развития нейродегенеративных заболеваний. Дан анализ различных модальностей, используемых для диагностики и мониторинга. Рассмотрены такие модальности, как рукописные данные, электроэнцефалограмма, речь, походка, движение глаз, а также использование композиций данных модальностей. Проведен подробный анализ современных методов и систем принятия решений, основанных на машинном обучении. Представлены наборы данных, методы предобработки, модели машинного обучения, оценки точности при диагностике заболеваний. В заключении рассмотрены текущие открытые проблемы и будущие перспективы исследований в данном направлении.Работа выполнена при поддержке Российского на-учного фонда (проект № 22-21-00021)

Sparsely Activated Networks: A new method for decomposing and compressing data

Recent literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features, but without considering the description length of the representations. In this thesis, first we introduce the{\phi}metric that evaluates unsupervised models based on their reconstruction accuracy and the degree of compression of their internal representations. We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize the previously defined metric $\varphi$. We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function. During decoding, the same weights are convolved with the sparse activation map and subsequently the partial reconstructions from each weight are summed to reconstruct the input. We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using $\varphi$ have small description representation length and consist of interpretable kernels.Comment: PhD Thesis in Greek, 158 pages for the main text, 23 supplementary pages for presentation, arXiv:1907.06592, arXiv:1904.13216, arXiv:1902.1112