Интеллектуальное кресло-робот со вспомогательными средствами связи с использованием откликов TEP и характеристик диапазона спектра более высокого порядка

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.В последние годы все больше внимания уделяется навигационным и коммуникационным системам на основе электроэнцефалограммы головного мозга для сообществ с разными возможностями. Для предоставления навигационной системе вспомогательных средств связи в работе предложен настраиваемый протокол, использующий вызванные мыслительные потенциалы, чтобы помочь сообществам с разными возможностями. Представлены функции, основанные на спектрах более высокого порядка, для классификации семи основных задач, таких как Вперед, Влево, Вправо, Да, НЕТ, Помощь и Расслабление, которые можно использовать для управления креслом-роботом, а также для связи с использованием необычной парадигмы. Предлагаемая система записывает восьмиканальный беспроводной сигнал электроэнцефалографии от десяти субъектов, в то время как субъект воспринимал семь различных задач. Записанные сигналы мозговых волн предварительно обрабатываются для удаления интерференционных волн и сегментируются на сигналы шести частотных диапазонов: дельта, тета, альфа, бета, гамма 1-1 и гамма 2. Сигналы полосы частот сегментируются на выборки кадров равной длины и используются для извлечения признаков с использованием оценки биспектра. Кроме того, статистические характеристики, такие как среднее значение биспектральной величины и энтропия с использованием области биспектра, извлекаются и формируются как набор характеристик. Извлеченные наборы функций проходят десятикратную перекрестную проверку с использованием классификатора многослойной нейронной сети. Результаты показали, что энтропия модели классификатора на основе характеристик биспектральной величины имеет максимальную точность классификации 84,71 %, а среднее значение модели классификатора на основе характеристик биспектральной величины – минимальную точность классификации 68,52 %

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing.

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05)

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications

The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex

Graph neural networks for electroencephalogram analysis

El objetivo de este trabajo es proporcionar un modelo capaz de identificar la enfermedad de Alzheimer y el Deterioro Cognitivo Leve (DCL) en registros de electroencefalogramas (EEG). A pesar de que los EEGs son una de las pruebas más pruebas utilizadas para los trastornos neurológicos, hoy en día el diagnóstico de estas enfermedades se basa en el comportamiento del paciente. comportamiento del paciente. Esto se debe a que la precisión de los expertos en el reconocimiento visual de los EEGs se estima en torno al 50%. Para resolver las dificultades de la tarea mencionada, esta tesis propone un modelo de Red Neural Gráfica (GNN) para clasificar a los sujetos utilizando únicamente las señales registradas. Para desarrollar el modelo final, primero propusimos varios procedimientos para construir gráficos a partir de las señales de EEGs, explorando diferentes formas de representar la conectividad entre canales, así como métodos para la extracción de de las características relevantes. Por el momento, no hay modelos GNN propuestos para la detección de Alzheimer o DCL. Por lo tanto, utilizamos arquitecturas empleadas en tareas similares y las modificamos para nuestro dominio específico. Por último, se evalúa un conjunto de combinaciones coherentes de grafos y modelos GNN bajo el mismo conjunto de métricas. Además, para las combinaciones con mejor rendimiento, se realiza un estudio del impacto de varios hiperparámetros se lleva a cabo. Con el fin de manejar todos los experimentos posibles, hemos desarrollado un marco de software para construir fácilmente construir los diferentes tipos de gráficos, crear los modelos y evaluar su rendimiento. La mejor combinación de construcción de grafos y diseño de modelos, basada en capas convolucionales de atención a los grafos, conduce a un 92,31% de precisión en la clasificación binaria de sujetos sanos y enfermos de Alzheimer y a un 87,59% de precisión cuando se evalúan también las grabaciones de pacientes con Deterioro Cognitivo Leve, que son comparables a los resultados del estado del arte. resultados del estado del arte. Aunque este trabajo se realiza en un campo novedoso y existen muchas posibilidades aún posibilidades aún por explorar, concluimos que las GNNs muestran capacidades sobrehumanas para la detección de Alzheimer y DCL utilizando EEGs.The aim of this work is to provide a model able to identify Alzheimer's disease and Mild Cognitive Impairment (MCI) in electroencephalogram's (EEGs) recordings. Despite EEGs being one of the most common tests used for neurological disorders, nowadays the diagnose of these diseases is based on the patient's behaviour. This is because expert's accuracy on EEGs visual recognition is estimated to be around 50%. To solve the difficulties of the aforementioned task, this thesis proposes a Graph Neural Network (GNN) model to classify the subjects using only the recorded signals. To develop the final model, first we proposed several procedures to build graphs from the EEGs signals, exploring different ways of representing the inter-channel connectivity as well as methods for relevant features extraction. For the time being, there are not GNN models proposed for Alzheimer or MCI detection. Hence, we used architectures employed by similar tasks and modified them for our specific domain. Finally, a set of coherent combinations of graph and GNN model is evaluated under the same set of metrics. Moreover, for the best performing combinations, a study of the impact of several hyperparameters is carried out. In order to handle all the possible experiments, we developed a software framework to easily build the different types of graphs, create the models and evaluate their performance. The best combination of graph building and model design, based on graph attention convolutional layers, leads to a 92.31% of accuracy in the binary classification of healthy subjects and Alzheimer's patients and to a 87.59% of accuracy when also evaluating MCI patients recordings, these are comparable to state of the art results. Although this work is done within a novel field and there exist many possibilities yet to be explored, we conclude that GNNs show super-human capabilities for Alzheimer and MCI detection using EEGs

Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels

Nowadays, many studies have been conducted to assess driver fatigue, as it has become one of the leading causes of traffic crashes. However, with the use of advanced features and machine learning approaches, EEG signals may be processed in an effective way, allowing fatigue to be detected promptly and efficiently. An optimal channel selection approach and a competent classification algorithm might be viewed as a critical aspect of efficient fatigue detection by the driver. In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. Moreover, power spectral density (PSD) was used to extract the feature, confirming the presented method's robustness. Additionally, to make the fatigue detection system faster, we conducted the experiment in three different time windows, including 0.5s, 0.75s, and 1s. It was found that the proposed method attained classification accuracy of 99.99% in a 0.5 second time window to identify driver fatigue by means of EEG. The outstanding performance of the presented framework can be used effectively in EEG-based driver fatigue detection

Graph Neural Network-based EEG Classification:A Survey

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.</p