Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM

Classification of electroencephalography using cooperative learning based on participating client balancing

Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods

Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accurac

A new method for accurate detection of movement intention from single channel EEG for online BCI

Low frequency readiness potential (RP) is elicited in electroencephalograms (EEGs) as one intends to perform an imagery (IMI) or real movement (RMI). While in most brain-computer-interface (BCI) applications the challenge is to classify RPs of different limbs from the given EEG trials, the objective of this study is fast and automatic detection of RPs from the entire single channel EEG signal. The proposed algorithm has two threshold blocks based on the nonlinear Teager-Kaiser energy operator (TEO) in the first block and the morphological properties of the RP waveform as constraints in the second block. The performance is strongly influenced by the abrupt energy changes due to transients and artefacts. As the major contribution, the proposed nonlinear convex optimization algorithm enables separation of transients from low frequency components by providing a fast thresholding mechanism. Application of the proposed method to Physionet RMI dataset, BCI competitionIV-1 IMI dataset and our own left hand movement datasets of healthy subjects led to true positive rates (TPRs) of 76.5±8.27%, 83.85±11.4%, and 81.1±5.23%, number of FPs/min of 2.4±1.07, 1.4±0.7, and 1.6±0.69 and accuracy rates of 85.4±3.83%, 90±3.56%, and 91.2±2.04%. Movement onset detection latency from our automatic RP detector was -384.9±296.5 ms. As a conclusion, the proposed method outperforms state-of-the-art techniques using as low as single channel EEG making it suitable for real-time neuro-rehabilitation of paralyzed subjects suffering from stroke

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

Методы классификации ЭЭГ-паттернов воображаемых движений

Рассматриваются наиболее перспективные методы классификации электроэнцефалографических сигналов при разработке неинвазивных интерфейсов мозг–компьютер и теоретических подходов для успешной классификации электроэнцефалографических паттернов. Приводится обзор работ, использующих для классификации риманову геометрию, методы глубокого обучения и различные варианты предобработки и кластеризации электроэнцефалографических сигналов, например общего пространственного фильтра. Среди прочих подходов предобработка электроэнцефалографических сигналов с применением общего пространственного фильтра часто используется как в офлайн, так и в онлайн режимах. Согласно исследованиям последних лет сочетание общего пространственного фильтра, линейного дискриминантного анализа, метода опорных векторов и нейронной сети с обратным распространением ошибки позволило достигнуть 91% точности при двухклассовой классификации с обратной связью в виде управления экзоскелетом. Исследований по использованию римановой геометрии в условиях онлайн очень мало, и на данный момент наилучшая точность при двухклассовой классификации составляет 69,3%. При этом в офлайн тестировании средний процент классификации в рассмотренных статьях для подходов с применением общего пространственного фильтра – 77,5±5,8%, сетей глубокого обучения – 81,7±4,7%, римановой геометрии – 90,2±6,6%. За счет нелинейных преобразований методы, основанные на римановой геометрии, а также на применении глубоких нейронных сетей сложной архитектуры, обеспечивают большую точность и способность к извлечению полезной информации из сигнала по сравнению с линейным преобразованием общего пространственного фильтра. Однако в условиях реального времени важна не только точность, но и минимальная временная задержка. Здесь преимущество может быть за подходами с использованием преобразования общего пространственного фильтра и римановой геометрии с временной задержкой менее 500 мс

Методы классификации ЭЭГ-паттернов воображаемых движений

The review focuses on the most promising methods for classifying EEG signals for non-invasive BCIs and theoretical approaches for the successful classification of EEG patterns. The paper provides an overview of articles using Riemannian geometry, deep learning methods and various options for preprocessing and "clustering" EEG signals, for example, common-spatial pattern (CSP). Among other approaches, pre-processing of EEG signals using CSP is often used, both offline and online. The combination of CSP, linear discriminant analysis, support vector machine and neural network (BPNN) made it possible to achieve 91% accuracy for binary classification with exoskeleton control as a feedback. There is very little work on the use of Riemannian geometry online and the best accuracy achieved so far for a binary classification problem is 69.3% in the work. At the same time, in offline testing, the average percentage of correct classification in the considered articles for approaches with CSP – 77.5 ± 5.8%, deep learning networks – 81.7 ± 4.7%, Riemannian geometry – 90.2 ± 6.6%. Due to nonlinear transformations, Riemannian geometry-based approaches and complex deep neural networks provide higher accuracy and better extract of useful information from raw EEG recordings rather than linear CSP transformation. However, in real-time setup, not only accuracy is important, but also a minimum time delay. Therefore, approaches using the CSP transformation and Riemannian geometry with a time delay of less than 500 ms may be in the future advantage.Рассматриваются наиболее перспективные методы классификации электроэнцефалографических сигналов при разработке неинвазивных интерфейсов мозг–компьютер и теоретических подходов для успешной классификации электроэнцефалографических паттернов. Приводится обзор работ, использующих для классификации риманову геометрию, методы глубокого обучения и различные варианты предобработки и кластеризации электроэнцефалографических сигналов, например общего пространственного фильтра. Среди прочих подходов предобработка электроэнцефалографических сигналов с применением общего пространственного фильтра часто используется как в офлайн, так и в онлайн режимах. Согласно исследованиям последних лет сочетание общего пространственного фильтра, линейного дискриминантного анализа, метода опорных векторов и нейронной сети с обратным распространением ошибки позволило достигнуть 91% точности при двухклассовой классификации с обратной связью в виде управления экзоскелетом. Исследований по использованию римановой геометрии в условиях онлайн очень мало, и на данный момент наилучшая точность при двухклассовой классификации составляет 69,3%. При этом в офлайн тестировании средний процент классификации в рассмотренных статьях для подходов с применением общего пространственного фильтра – 77,5±5,8%, сетей глубокого обучения – 81,7±4,7%, римановой геометрии – 90,2±6,6%. За счет нелинейных преобразований методы, основанные на римановой геометрии, а также на применении глубоких нейронных сетей сложной архитектуры, обеспечивают большую точность и способность к извлечению полезной информации из сигнала по сравнению с линейным преобразованием общего пространственного фильтра. Однако в условиях реального времени важна не только точность, но и минимальная временная задержка. Здесь преимущество может быть за подходами с использованием преобразования общего пространственного фильтра и римановой геометрии с временной задержкой менее 500 мс

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art