Classification Of EEG Signals Research Articles

Competing Visual Cues Revealed by Electroencephalography: Sensitivity to Motion Speed and Direction.

Motion speed and direction are two fundamental cues for the mammalian visual system. Neurons in various places of the neocortex show tuning properties in term of firing frequency to both speed and direction. The present study applied a 32-channel electroencephalograph (EEG) system to 13 human subjects while they were observing a single object moving with different speeds in various directions from the center of view to the periphery on a computer monitor. Depending on the experimental condition, the subjects were either required to fix their gaze at the center of the monitor while the object was moving or to track the movement with their gaze; eye-tracking glasses were used to ensure that they followed instructions. In each trial, motion speed and direction varied randomly and independently, forming two competing visual features. EEG signal classification was performed for each cue separately (e.g., 11 speed values or 11 directions), regardless of variations in the other cue. Under the eye-fixed condition, multiple subjects showed distinct preferences to motion direction over speed; however, two outliers showed superb sensitivity to speed. Under the eye-tracking condition, in which the EEG signals presumably contained ocular movement signals, all subjects showed predominantly better classification for motion direction. There was a trend that speed and direction were encoded by different electrode sites. Since EEG is a noninvasive and portable approach suitable for brain-computer interfaces (BCIs), this study provides insights on fundamental knowledge of the visual system as well as BCI applications based on visual stimulation.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.

Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals and has a significant impact on the lives of their families. Therefore, the development of reliable diagnostic tools for the early detection of this condition is considered beneficial to alleviate the social and emotional distress experienced by patients. While the Bonn University dataset contains five collections of EEG data, not many studies specifically focus on subsets D and E. These subsets correspond to EEG recordings from the epileptogenic zone during ictal and interictal events. In this work, the parallel ictal-net (PIN) neural network architecture is introduced, which utilizes scalograms obtained through a continuous wavelet transform to achieve the high-accuracy classification of EEG signals into ictal or interictal states. The results obtained demonstrate the effectiveness of the proposed PIN model in distinguishing between ictal and interictal events with a high degree of confidence. This is validated by the computing accuracy, precision, recall, and F1 scores, all of which consistently achieve around 99% confidence, surpassing previous approaches in the related literature.

Advanced framework for epilepsy detection through image-based EEG signal analysis.

Recurrent and unpredictable seizures characterize epilepsy, a neurological disorder affecting millions worldwide. Epilepsy diagnosis is crucial for timely treatment and better outcomes. Electroencephalography (EEG) time-series data analysis is essential for epilepsy diagnosis and surveillance. Complex signal processing methods used in traditional EEG analysis are computationally demanding and difficult to generalize across patients. Researchers are using machine learning to improve epilepsy detection, particularly visual feature extraction from EEG time-series data. This study examines the application of a Gramian Angular Summation Field (GASF) approach for the analysis of EEG signals. Additionally, it explores the utilization of image features, specifically the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) techniques, for the purpose of epilepsy detection in EEG data. The proposed methodology encompasses the transformation of EEG signals into images based on GASF, followed by the extraction of features utilizing SIFT and ORB techniques, and ultimately, the selection of relevant features. A state-of-the-art machine learning classifier is employed to classify GASF images into two categories: normal EEG patterns and focal EEG patterns. Bern-Barcelona EEG recordings were used to test the proposed method. This method classifies EEG signals with 96% accuracy using SIFT features and 94% using ORB features. The Random Forest (RF) classifier surpasses state-of-the-art approaches in precision, recall, F1-score, specificity, and Area Under Curve (AUC). The Receiver Operating Characteristic (ROC) curve shows that Random Forest outperforms Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers. The suggested method has many advantages over time-series EEG data analysis and machine learning classifiers used in epilepsy detection studies. A novel image-based preprocessing pipeline using GASF for robust image synthesis and SIFT and ORB for feature extraction is presented here. The study found that the suggested method can accurately discriminate between normal and focal EEG signals, improving patient outcomes through early and accurate epilepsy diagnosis.

Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis

Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, and long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizures. Impact Statement- This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detection is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, and sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summarizing it will give a new prospective to the reader.

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

Enhancing MI EEG Signal Classification With a Novel Weighted and Stacked Adaptive Integrated Ensemble Model: A Multi-Dataset Approach

Enhancing MI EEG Signal Classification With a Novel Weighted and Stacked Adaptive Integrated Ensemble Model: A Multi-Dataset Approach

Enhancing Medical Signal Processing and Diagnosis with AI-Generated Content Techniques.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

Alzheimer's Disease Detection in EEG Sleep Signals.

Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the clinical scenario characterized by the limited data availability. The methodology entails testing and comparing the performance of semi-supervised models, benchmarked against an unsupervised and a supervised model. The study highlights the significance of spatial and temporal analysis capabilities, conducting independent analyses of each sleep stage. Results demonstrate the effectiveness of one semi-supervised model in leveraging limited labeled data, achieving stable metrics across all sleep stages, and reaching 90% accuracy in its supervised form. Comparative analyses reveal this superior performance over the unsupervised model, while the supervised model ranges between 92-94% . These findings underscore the potential of semi-supervised models in early AD detection, particularly in overcoming the challenges associated with the scarcity of labeled data. Ablation tests affirm the critical role of spatio-temporal feature extraction in semi-supervised predictive performance, and t-SNE visualizations validate the model's proficiency in distinguishing AD patterns. Overall, this research contributes to the advancement of AD detection through innovative Deep Learning approaches, highlighting the crucial role of semi-supervised learning in addressing data limitations.

A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

Enhancing EEG Signal Classification With a Novel Random Subset Channel Selection Approach: Applications in Taste, Odor, and Motor Imagery Analysis

Enhancing EEG Signal Classification With a Novel Random Subset Channel Selection Approach: Applications in Taste, Odor, and Motor Imagery Analysis

A novel brain EEG clustering based on Minkowski distance to improve intelligent epilepsy diagnosis

This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG feature selection for classification and have efficient Particle Swarm Optimization (PSO) in the context of machine learning. Given the intricacy of high-dimensional medical datasets, feature selection plays a critical role in preventing disease and promoting public health. By employing Minkowski clustering, the objective is to group dataset records into two clusters exhibiting high feature coherence, thereby improving accuracy by applying optimization techniques like PSO to select the most optimal features. Furthermore, the proposed model can be extended to intelligent datasets, including EEG and others. As fewer features are needed for precise categorization, intelligent feature selection is an advanced step of machine learning. This paper investigates the key factors influencing feature selection in the EEG Bonn University dataset. The proposed system is compared against various optimization and feature selection methods, demonstrating superior performance in analyzing and classifying EEG signals based on accuracy measures. The experimental results have confirmed the effectiveness of the suggested model as a valuable tool for EEG data classification, achieving up to 100% accuracy. The outcomes of this research have the potential to benefit medical experts in related specialties by streamlining the process of identifying and diagnosing brain disorders. Technically, the machine learning algorithms RF, KNN, SVM, NB, and DT are employed to classify the selected features.

Classification of Epileptic EEG Signals Using Improved Atomic Search Optimization Algorithm

Classification of Epileptic EEG Signals Using Improved Atomic Search Optimization Algorithm

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

Dynamic decomposition graph convolutional neural network for SSVEP-based brain–computer interface

The SSVEP-based paradigm serves as a prevalent approach in the realm of brain–computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Brain–computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms.

Application of graph frequency attention convolutional neural networks in depression treatment response.

Depression, a prevalent global mental health disorder, necessitates precise treatment response prediction for the improvement of personalized care and patient prognosis. The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique for handling intricate signals and classification tasks owing to their end-to-end neural architecture and nonlinear processing capabilities. In this context, this article proposes a model named the Graph Frequency Attention Convolutional Neural Network (GFACNN). Primarily, the model transforms the EEG signals into graphs to depict the connections between electrodes and brain regions, while integrating a frequency attention module to accentuate brain rhythm information. The proposed approach delves into the application of graph neural networks in the classification of EEG data, aiming to evaluate the response to antidepressant treatment and discern between treatment-resistant and treatment-responsive cases. Experimental results obtained from an EEG dataset at Peking University People's Hospital demonstrate the notable performance of GFACNN in distinguishing treatment responses among depression patients, surpassing deep learning methodologies including CapsuleNet and GoogLeNet. This highlights the efficacy of graph neural networks in leveraging the connections within EEG signal data. Overall, GFACNN exhibits potential for the classification of depression EEG signals, thereby potentially aiding clinical diagnosis and treatment.

Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.

Classification of epileptic EEG signals with the utilization of Bonferroni mean based fuzzy pattern tree

The Electroencephalogram (EEG) is an essential tool used to detect and investigate multiple neurological disorders within the human brain. However, examination and visualization of any such abnormalities in the brain through inspecting these EEG signals is a time-consuming and repetitive task for a neurologist. Hence, there is a need to develop a model that can automatically classify brain disorders based on the EEG signals. In this study, a novel fuzzy classification model is proposed for classifying the EEG signals. First, the discrete wavelet transform (DWT) technique is employed to decompose the signal into time–frequency sub-bands. Then, by utilizing these sub-bands, seven meaningful statistical features are extracted effectively for additional investigation purposes. Then, the retrieved features are provided to the fuzzy pattern tree classifier to obtain meaningful inferences from the data. Here, a distinctive structure of a fuzzy pattern tree is proposed by utilizing the interrelationship handling aggregation operators. More precisely, we utilize the Bonferroni mean (BM) aggregation operator for the enlargement or expansion of the tree while considering the concatenation between the parent and the slave tree. To the best of our knowledge, this is the first work that presents such an interrelationship handling fuzzy logic-based classifier model for EEG signal classification. The proposed algorithm is evaluated using two publicly available datasets, namely the Bonn University (Bonn) epileptic datasets and the Temple University Hospital (TUH) EEG subcorpus of abnormal (TUAB) and epilepsy (TUEP) datasets. This study considers different frames of the binary problem (i.e., healthy vs. seizure detection) and multi-class problem (i.e., healthy vs. seizure free vs. seizure) over the Bonn dataset and achieves more than 99% accuracy over the binary model by testing over all possible combinations of the available experimental dataset and 97.8% accuracy in multi-class model. In the case of TUAB and TUEP categories, the model achieves an accuracy of 88% in predicting normal to abnormal activities and 86% in detecting epilepsy and non-epilepsy within the signals. The outcomes derived from the proposed model outperform some existing studies available in the literature for EEG signal classification. Also, the model is efficient as it considers the uncertainty and association among the features while framing the classifier model.

Time and frequency domain pre-processing for epileptic seizure classification of epileptic EEG signals

Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it’s side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure’s ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all.