Neural network analysis of electroencephalograms based on their graphical representation

The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF) and Markov Transition Field (MTF) transformations are used to represent time series as images. The paper shows the possibility of using GAF and MTF EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface

Electroencephalogram Analysis Based on Gramian Angular FieldTransformation

This paper addresses the problem of motion imagery classification from electroencephalogram signals which related with manydifficulties such on human state, measurement accuracy, etc. Artificial neural networks are a good tool to solve such kind of problems.Electroencephalogram is time series signals therefore, a Gramian Angular Fields conversion has been applied to convert it into images.GAF conversion was used for classification EEG with Convolutional Neural Network (CNN). GAF images are represented as a Gramianmatrix where each element is the trigonometric sum between different time intervals. Grayscale images were applied for recognition toreduce numbers of neural network parameters and increase calculation speed. Images from each measuring channel were connectedinto one multi-channel image. This article reveals the possible usage GAF conversion of EEG signals to motion imagery recognition,which is beneficial in the applied fields, such as implement it in brain-computer interfac

Utilizing Deep Neural Networks for Brain–Computer Interface-Based Prosthesis Control

Limb amputations affect a significant portion of the world’s population every year. The necessity for these operations can be associated with related health conditions or a traumatic event. Currently, prosthetic devices intended to alleviate the burden of amputation lack many of the premier features possessed by their biological counterparts. The foremost of these features are agility and tactile function. In an effort to address the former, researchers here investigate the fundamental connection between agile finger movement and brain signaling. In this study each subject was asked to move his or her right index finger in sync with a time-aligned finger movement demonstration while each movement was labeled and the subject’s brain waves were recorded via a single-channel electroencephalograph. This data was subsequently used to train and test a deep neural network in an effort to classify each subject’s intention to rest and intention to extend his or her right index finger. On average, the employed model yielded an accuracy of 63.3%, where the most predictable subject’s movements were classified with an accuracy of 70.5%

Artificial Intelligence-based Detection of Epileptic Discharges from Pediatric Scalp Electroencephalograms: A Pilot Study

We developed an artificial intelligence (AI) technique to identify epileptic discharges (spikes) in pediatric scalp electroencephalograms (EEGs). We built a convolutional neural network (CNN) model to automatically classify steep potential images into spikes and background activity. For the CNN model’ training and validation, we examined 100 children with spikes in EEGs and another 100 without spikes. A different group of 20 children with spikes and 20 without spikes were the actual test subjects. All subjects were ≥ 3 to 0.97 when referential and combination EEG montages were used, and 0.99, indicating high performance of the classification method. EEG patterns that interfered with correct classification included vertex sharp transients, sleep spindles, alpha rhythm, and low-amplitude ill-formed spikes in a run. Our results demonstrate that AI is a promising tool for automatically interpreting pediatric EEGs. Some avenues for improving the technique were also indicated by our findings

Predicting sex from brain rhythms with deep learning

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10-5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology

Motor imagery recognition in electroencephalograms using convolutional neural networks

Электроэнцефалография является широко распространенным методом для получения сигналов головного мозга, для снятия которых используются электроды, расположенные на поверхности головы. Такой метод регистрации мозговой активности стал популярен благодаря относительной дешевизне, компактности, а также из-за отсутствия необходимости имплантировать электроды непосредственно в мозг. Статья посвящена проблеме распознавания моторных образов по сигналам электроэнцефалограмм. Природа таких сигналов носит комплексный характер. Характеристики электроэнцефалограмм зависят от самого человека, его возраста, психического состояния, присутствия шумов и помех. При их анализе необходимо учитывать множество таких параметров. Искусственные нейронные сети являются хорошим инструментом в решении такого класса задач. Их применение позволяет объединить задачи извлечения, выбора и классификации признаков в одном блоке обработки сигналов. Электроэнцефалограммы представляют собой временные сигналы. Для представления таких сигналов в виде изображений применяются преобразования на основе матрицы Грама и Марковской матрицы перехода. В статье показана возможность применения этих преобразований для распознавания моторных образов на примере воображаемых движений правой и левой рукой, а также исследовано влияние разрешения получаемых изображений на точность классификации. Наилучшая точность классификации сигнала электроэнцефалограммы на классы движения и состояния покоя составляет порядка 99 %. Результаты исследований в дальнейшем могут быть применены при построении интерфейса мозг − компьютер.Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта No 18-08-00977 А и было поддержано Программой повышения конкурентоспособности Томского политехнического университета

Feature extraction with GMDH-type neural networks for EEG-based person identification

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant (p<0.01) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification

Survey on encode biometric data for transmission in wireless communication networks

The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication

Influencing brain waves by evoked potentials as biometric approach: taking stock of the last six years of research

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area