EEG Signal Processing Research Articles

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology.

Research on Fatigue Driving Detection Technology Based on CA-ACGAN.

Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.

Playing Tetris with Your Mind: A BCI-Controlled Gaming Experience

Brain-Computer Interface Controlled Tetris: Exploring Feasibility and User Experience. This project explores the feasibility and user experience of controlling a Tetris game using a Brain-Computer Interface (BCI). The BCI system utilizes non-invasive Electroencephalography (EEG) to detect P300 brainwaves generated by user focus on specific on-screen cues corresponding to desired game actions (left, right, rotate, drop).An ESP8266 microcontroller board, EEG sensor, and display are used to create a portable BCI system. Software development leverages open-source libraries for EEG signal processing, classification, and game engine control (onboard or external).The experiment evaluates BCI control performance through metrics like accuracy, speed, and Tetris completion rate. A control group playing traditional Tetris (if applicable) allows for comparison. Subjective user experience is assessed through questionnaires and surveys. The results are expected to demonstrate the feasibility of BCI- controlled Tetris using affordable hardware. Potential trade-offs between BCI control speed and traditional methods will be explored. User experience analysis will provide insights into the challenge, enjoyment, and future directions of BCI gaming. This project contributes to the growing field of BCI applications by demonstrating its potential for novel and mind-controlled gaming experiences. Keywords: Brain-Computer Interface, Electroencephalography, performance.

Optimized wavelet and feature set of EEG signal for Parkinson disease classification

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination.

Emerging Trends in EEG Signal Processing: A Systematic Review

Emerging Trends in EEG Signal Processing: A Systematic Review

Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification

Abstract The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.

A Review on Deep Learning For Electroencephalogram Signal Classification

Recently, the research on Electroencephalogram (EEG) signals have been obtained more focus at the same time the EEG signal is regarded as the basis for the prediction of diagnosis disease and the brain behavior. EEG is as significant tool for many conditions that can be recorded the brain human waves which accommodate the brain activity. In the recent decades, EEG data has been extensively applied in the approaches of data analysis such as time series analysis. With the considerable achievement of deep learning (DL) implement on the time series data, multiple studies have been began applying deep learning algorithms on the processing of EEG signal. Several deep learning techniques that assistant in the detection various psycho-neuro disorders, have been proposed in order to automate EEG detection and classification with great development in multiple applications of EEG signals. Also, different machine learning (ML) algorithms have been presented in such research for brain signals identification and classification in the era of Artificial intelligence (AI). In an attempt to summarize the EEG signal processing techniques, we have performed a literature review around deep learning algorithms for decoding the human’s brain activity as well as diagnosis disease and clarified particulars about several deep learning algorithms. We also conducted some of ML papers about EEG signals classification. Based on the achievement results of the research mentioned in this article appears an advanced scientific development in terms of deep learning.

AN EPILEPSY DIAGNOSIS METHOD BASED ON A TRANSFERRED ALEXNET MODEL AND EEG SIGNALS

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches.

Local spatial analysis of EEG signals using the Laplacian montage

Objectives. One pressing problem when recording brain activity signals by electroencephalography (EEG) is the need to reduce the effect of interference (artifacts). This study presents a method for resolving this problem using the Laplace differential operator. The aim is to determine the number of electrodes included in the Laplacian montage, as well as to clarify the requirements for the geometric shape of their placement, in order to ensure the best quality of EEG signal processing.Methods. The Laplacian montage method is based on the use of individual electrodes to determine the second derivative of the signal, proportional to the electric current at the corresponding point on the surface of the head. This approach allows the potential of neural activity of the source located in a small area limited by the electrode complex to be evaluated. By using a small number of equidistant electrodes placed around the target electrode, the Laplacian montage can produce a significantly higher quality signal from the area under the electrode complex.Results. Among all the methods for constructing the Laplacian montage discussed in the article, a complex consisting of 16 + 1 electrodes was shown to be preferable. The choice of the 16 + 1 scheme was determined by the best compromise between the quality of EEG signal processing and the complexity of manufacturing the electrode complex with given geometric parameters. The quality assessment was carried out by simulating the interference signal which allowed the correctness of the choice of installation design to be evaluated.Conclusions. The use of the Laplacian montage method can significantly reduce the effect of artifacts. The proposed montage scheme ensures a good suppression of interference signals, the sources of which are located far beyond the projection of the electrode complex. However, not all interference arising from sources deep inside the brain, can be effectively suppressed using the Laplacian montage scheme alone.

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.

MBPPE: A Modular Batch Processing Platform for Electroencephalography

To ensure the accuracy and reliability of subsequent analysis, research on electroencephalogram (EEG) signals typically requires preliminary processing of large datasets to eliminate noise and artifacts. Traditional batch processing methods require substantial hardware resources while lacking flexible automated workflows and user-friendly interactions. To address these challenges, we have implemented a modular batch processing platform for EEG (MBPPE) that offers both local execution and private deployment options to meet the demands of efficient signal processing from individuals to laboratories. We modularize the processing methods and organize them into pluggable multi-task batch processes, providing asynchronous processing solutions. In addition, we extend user functions by introducing plugins and promoting collaborative interaction through data sharing, access control, and comment communication. Simultaneously, interactive features are integrated into the visualization design, enabling users to process and analyze data more intuitively and naturally. Currently, the platform integrates several commonly used data preprocessing and analysis techniques, providing a novel solution for batch processing of EEG signals.

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series

BackgroundIndividuals in the early stages of Alzheimer’s Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. New methodsThis paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. ResultsResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. Comparison with existing methodsResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. ConclusionDeep learning architectures hold great promise in enhancing the accuracy of early MCI detection.

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

ОБЗОР СПОСОБОВ ИЗМЕРЕНИЯ КОГНИТИВНОЙ НАГРУЗКИ МОЗГА И МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ИХ ИДЕНТИФИКАЦИИ НА ОСНОВЕ ДАННЫХ ЭЭГ

Aim. The purpose of this article is review and analyze methods for measuring cognitive load, as well as approaches to using machine learning techniques to identify EEG data. Materials and methods. The review systematizes and summarizes the information on the topic under consideration. Scientific articles were searched in bibliographic databases: eLIBRARY, ScienceDirect, Scopus. Results. This review focused on ways to measure the cognitive load of the brain, modern EEG recording devices, and methods for transforming, extracting, and classifying features from acquired EEG signals. Conclusion. With new wearable devices available for acquiring and processing EEG signals, there is a need to develop new approaches for using machine learning to identify cognitive brain processes.

EEG Source Imaging-Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution.

EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

Method and software for processing daily EEG signals for detection of epileptic seizures in humans

A method, an algorithm and a software tool for processing daily EEG signals for computer electroencephalographic systems to detect the manifestation of epileptic seizures in humans have been developed. Mathematically, the daily EEG signal is presented as a random sequence of white Gaussian noise zones and additive mixtures of different-frequency harmonic components. Harmonic functions interpret the manifestations of epileptic seizures. The core of the method of processing daily EEG signals is a time-shifted window inter-covariance processing with multiple kernels in the form of different-frequency harmonic functions. Based on the method of window processing, an algorithm and a software tool for daily EEG signal processing with a graphical user interface using the MATLAB environment have been implemented. The developed software can be used as a component of computer EEG systems. The results of daily EEG signal processing using the software are displayed in the form of averaged products of covariance results (the value is measured in power units) within each processing window, which quantitatively reflect the time points of epileptic seizures in a person. Manifestations of epileptic seizures are reflected through the increase in the averaged values of the power of covariances in relation to observation intervals without corresponding manifestations of these seizures. To ensure the authorization of the process of determining the level of decision-making regarding the moments of epileptic seizures (exceeding the normal level), the threshold algorithm and the Neumann-Pearson statistical criterion were applied.

Research on the Creative Performance of Digital Film and Television Works Based on Virtual Reality Technology

Abstract This study investigates the application of virtual reality technology in the creative expression of digital film and television productions, especially the role of EEG signal denoising and feature recognition methods in enhancing the audience experience. The study uses wavelet threshold denoising and parallel RLS adaptive filtering algorithms to process EEG signals to improve the accuracy and reliability of the data. Then, the EEG signals were feature extracted using a bihemispheric domain adversarial neural network (BiDANN) to more accurately recognize the user’s emotional responses. The experimental results show that in the virtual reality environment, the users’ concentration and emotional reactions are significantly improved, with the average concentration reaching 74.21 and the average value of the electrodermal test data being 6.19. In addition, the eye-movement interaction experiments show that different types of digital movie and television works can cause additional attention allocation of users in the VR environment, leading other creative performance effects. The study’s results prove that virtual reality technology can significantly enhance the innovative performance of digital movie and television works and improve the audience’s viewing experience.

A Detailed Study on IIR-FIR Filters and Design of a Graphical User Interface for Simulation of EEG Signals

Electroencephalography (EEG) plays a pivotal role in accepting brain activity and identifying neurological disorders. The precise analysis and explanation of EEG signals require the solicitation of digital signal processing techniques, such as Infinite Impulse Response (IIR) as well as Finite IJmpulse Response (FIR) filters. This paper investigates into a complete exploration of IIR-FIR filters and their application in EEG signal processing. The first part of this research paper involves a detailed examination of IIR and FIR filter types, their mathematical foundations, pros, and cons. Various filter design methods, such as Chebyshev, Butterworth and Elliptic filters, are discussed as well as compared through literature survey. Speculative aspects, including filter design, transfer functions, and frequency responses, are presented in a clear and much accessible manner. The second phase of the study introduces the design of a Graphical User Interface (GUI) on mathematical modelling tool aimed at enabling EEG signal simulation and analysis. This GUI is designed to enable users, including researchers and clinicians, to generate synthetic EEG signals with controllable parameters, apply IIR-FIR filters in real-time, and thereby visualize the filtered signals. The interface offers user-friendly controls for customizing filter characteristics, such as filter order, cutoff frequencies, and filter type. To validate the efficiency of the designed GUI and the selected IIR-FIR filters, general simulations are conducted using EEG datasets. The results showcase the GUI's efficacy in real-time EEG signal processing, demonstrating its prospective in research, clinical diagnostics, and educational settings and many areas. In summary, this paper presents a full investigation into IIR-FIR filters, proposing insights into their theory and practical application for EEG signal processing. The development of a intelligible GUI for EEG signal simulation and study further enhances the approachability of these mathematical modelling tools to a wider audience, eventually contributing to progressions in the field of neuroscience and brain signal analysis.

EEG Signal Filtering Method and Comparison Between Modern to Classical Filter

In neuroscience and therapeutic applications, electroencephalography (EEG) has become a crucial technique for gaining understanding of brain activity and cognitive processes. In order to improve signal quality, lower noise, and extract useful information from EEG data, preprocessing is essential. In the context of EEG signal processing, this study compares the effectiveness of three frequently used filtering methods: the median filter, average filter, and Gaussian filter.This study's main goal is to assess and compare how well these filters work to reduce the various forms of noise that may be found in EEG data, such as physiological artifacts, environmental interference, and electrode noise. The study makes use of a large dataset of EEG recordings that spans a range of experimental settings and mental states. The filtering methods are methodically applied to the unprocessed EEG data, and then the effects on signal fidelity and quality are thoroughly examined.The findings of this investigation shed important light on the advantages and disadvantages of each filtering strategy used in EEG data processing. In the end, the conclusions from this comparison study help offer criteria for choosing a suitable filtering approach based on the unique properties of EEG data and the research goals.The findings of this investigation offer important new understandings of the advantages and disadvantages of each filtering strategy used in EEG data processing. Finally, depending on the unique properties of the EEG data and the study goals, the findings from this comparison analysis help offer criteria for choosing an acceptable filtering approach.

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.