EEG Detection Research Articles

Brain-Computer Interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection.

The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 μm for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.

Intraoperative Burst Suppression by Analysis of Raw Electroencephalogram Postoperative Delirium in Older Adults Undergoing Spine Surgery: A Retrospective Cohort Study.

Postoperative delirium is a common complication in older adults, associated with poor outcomes, morbidity, mortality, and higher health care costs. Older age is a strong predictor of delirium. Intraoperative burst suppression on the electroencephalogram (EEG) has also been linked to postoperative delirium and poor neurocognitive outcomes. In this a secondary analysis of data from the Perioperative Anesthesia Neurocognitive Disorder Assessment-Geriatric (PANDA-G) observational study, the raw EEGs of 239 spine surgery patients were evaluated. Associations between delirium and age, device-generated burst suppression ratio, and visual detection of the raw EEG were compared. Demographics and anesthesia durations were similar in patients with and without delirium. There was a higher incidence of burst suppression identified by analysis of the raw EEG in the delirium group than in the no delirium group (73.45% vs. 50.9%; P=0.001) which appeared to be driven largely by a higher incidence of burst suppression during maintenance of anesthesia (67.2% vs. 46.3%; P=0.004). Burst suppression was more strongly associated with delirium than with age; estimated linear regression coefficient for burst suppression 0.182 (SE: 0.057; P=0.002) and for age 0.009 (SE: 0.005; P=0.082). There was no significant interaction between burst suppression and age (-0.512; SE: 0.390; P=0.190). Compared with visual detection of burst suppression, the burst suppression ratio overestimated burst suppression at low values, and underestimated burst suppression at high values. Intraoperative burst suppression identified by visual analysis of the EEG was more strongly associated with delirium than age in older adults undergoing spine surgery. Further research is needed to determine the clinical importance of these findings.

A new fuzzy-based ensemble framework based on attention-based deep learning architectures for automated detection of abnormal EEG

A new fuzzy-based ensemble framework based on attention-based deep learning architectures for automated detection of abnormal EEG

Performance Analysis of KNN, Naïve Bayes, and Extreme Learning Machine Techniques on EEG Signals for Detection of Parkinson's Disease

The application of bio-potentials for diagnosing neurological disorders has become highly effective nowadays. This work focuses on using Electroencephalogram (EEG) to detect Parkinson's disease (PD), a significant neurological disorder. PD is considered the second most common neurological disorder in the world. Being degenerative in nature, it affects the patients progressively. The progression of the severity of this disease can be restricted by a certain limit if its symptoms can be well-treated on time. This work presents a relative analysis of the performances of three machine learning (ML) techniques in detecting PD. These are K-nearest Neighbor (KNN), Naïve Bayes and Extreme Learning Machine (ELM) techniques. Statistical-based features are evaluated from the EEG data signals of normal as well as persons with PD after preprocessing. The features evaluated are then classified using the three techniques. The results of the classifiers are evaluated with the help of some performance parameters such as accuracy, precision, sensitivity, specificity and F1 score. Based on the values of these parameters, the performances of all these techniques are compared. The comparison shows that ELM performs the best, with an accuracy of 98.84% in detecting PD. The reported methodology holds significant clinical relevance. It can offer an early, non-invasive, and objective method for diagnosing, tracking, and managing PD.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Retraction notice: Automatic diseases detection and classification of EEG signal with pervasive computing using machine learning

Retraction notice: Automatic diseases detection and classification of EEG signal with pervasive computing using machine learning

Emotion Detection from EEG Signals Using Machine Deep Learning Models.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

A novel hybrid Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM).

Concurrent recording of EEG/fMRI signals reveals cross-scale neurovascular dynamics that are crucial for elucidating fundamental linkage between function and behaviors. However, MRI scanners generate tremendous artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background. Here, a Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM) is developed to encode fMRI and EEG signals on distinct sidebands of the detector oscillation carrier wave for detection by a standard MRI console over the entire duration of fMRI sequence. Local field potential (LFP) and fMRI maps are retrieved through low-pass and high-pass filtering of frequency-demodulate signals. From optogenetically-stimulated somatosensory cortex, the positive correlation between evoked LFP and fMRI signals validates strong neurovascular coupling, enabling cross-scale brain mapping with this 2-in-1 transducer as a research and diagnostic tool.

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.

Epilepsy detection based on multi-head self-attention mechanism.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization.

Retraction Note: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Retraction Note: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Exploring BMI recognition through resting state, free imagination, and visual stimulation-induced EEG

In recent years, the development of Brain-Computer Interface (BCI) technology has attracted significant attention, particularly in the acquisition and application of electroencephalogram (EEG) signals. Most pertinent studies have utilized EEG to monitor neural activity states. Given that EEG does not provide direct insights into the physical states of body structures, many researchers have refrained from employing EEG to assess structural body states such as Body Mass Index (BMI). This study, for the first time, employs portable EEG detection devices combined with machine learning algorithms such as Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) to process EEG features, enabling the application of EEG in identifying BMI. We collected EEG data from three different states: resting state, free imagination, and viewing pictures. The results demonstrate that these EEG signals can effectively differentiate subjects with higher BMI. Particularly under visual stimulation, EEG features exhibited optimal performance when subjects viewed images of high-calorie foods, with the model achieving an average accuracy of 80% and an F1 score of 75%. From the perspective of frequency bands, within a single frequency band, the β band performed notably well in classification. However, the model constructed by integrating features from various frequency bands yielded the best classification performance. These comprehensive paradigms are expected to fill the current research gap in the application of EEG and provide new methodological perspectives for further studies.

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation

BackgroundVirtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. ObjectiveIn this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. MethodsThe model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. Results: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SignificanceIt shows that this model can be used for the recognition of VRMS based on the resting state EEG.

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

Low-Quality Video Target Detection Based on EEG Signal Using Eye Movement Alignment.

The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.

Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy.

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.

Theoretical and Technical Issues Concerning the Measurement of Alpha Frequency and the Application of Signal Detection Theory: Comment on Buergers and Noppeney (2022).

Classical and recent evidence has suggested that alpha oscillations play a critical role in temporally discriminating or binding successively presented items. Challenging this view, Buergers and Noppeney [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022] found that by combining EEG, psychophysics, and signal detection theory, neither prestimulus nor resting-state alpha frequency influences perceptual sensitivity and bias in the temporal binding task. We propose the following four points that should be considered when interpreting the role of alpha oscillations, and especially their frequency, on perceptual temporal binding: (1) Multiple alpha components can be contaminated in conventional EEG analysis; (2) the effect of alpha frequency on perception will interact with alpha power; (3) prestimulus and resting-state alpha frequency can be different from poststimulus alpha frequency, which is the frequency during temporal binding and should be more directly related to temporal binding; and (4) when applying signal detection theory under the assumption of equal variance, the assumption is often incomplete and can be problematic (e.g., the magnitude relationships between individuals in parametric sensitivity may change when converted into nonparametric sensitivity). Future directions, including solutions to each of the issues, are discussed.

Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.

Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning. These are presented in the first section of this review by Dr Wesley Kerr. (2) Provide insights into their cutting-edge applications in screening for medications in neural organoids, in general, and for epilepsy in particular. These are presented by Dr Sandra Acosta. (3) Provide insights into how artificial intelligence approaches can predict clinical response to medication treatments. These are presented by Dr Patrick Kwan. (4) Finally, provide insights into the expanding applications to the detection and analysis of EEG signals in intensive care, epilepsy monitoring unit, and intracranial monitoring situations, as presented below by Dr Gregory Worrell. The expectation is that, in the coming decade and beyond, the increasing use of the above approaches will transform epilepsy research and care and supplement, but not replace, the diligent work of epilepsy clinicians and researchers.

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.