Search Results (40)

Background/Objectives: This article addresses the challenge of stress detection across diverse contexts. Mental stress is a worldwide concern that substantially affects human health and productivity, rendering it a critical research challenge. Although numerous studies have investigated stress detection through machine learning (ML) techniques, there has been limited research on assessing ML models trained in one context and utilized in another. The objective of ML-based stress detection systems is to create models that generalize across various contexts. Methods: This study examines the generalizability of ML models employing EEG recordings from two stress-inducing contexts: mental arithmetic evaluation (MAE) and virtual reality (VR) gaming. We present a data collection workflow and publicly release a portion of the dataset. Furthermore, we evaluate classical ML models and their generalizability, offering insights into the influence of training data on model performance, data efficiency, and related expenses. EEG data were acquired leveraging MUSE-S^TM hardware during stressful MAE and VR gaming scenarios. The methodology entailed preprocessing EEG signals using wavelet denoising mother wavelets, assessing individual and aggregated sensor data, and employing three ML models—linear discriminant analysis (LDA), support vector machine (SVM), and K-nearest neighbors (KNN)—for classification purposes. Results: In Scenario 1, where MAE was employed for training and VR for testing, the TP10 electrode attained an average accuracy of 91.42% across all classifiers and participants, whereas the SVM classifier achieved the highest average accuracy of 95.76% across all participants. In Scenario 2, adopting VR data as the training data and MAE data as the testing data, the maximum average accuracy achieved was 88.05% with the combination of TP10, AF8, and TP9 electrodes across all classifiers and participants, whereas the LDA model attained the peak average accuracy of 90.27% among all participants. The optimal performance was achieved with Symlets 4 and Daubechies-2 for Scenarios 1 and 2, respectively. Conclusions: The results demonstrate that although ML models exhibit generalization capabilities across stressors, their performance is significantly influenced by the alignment between training and testing contexts, as evidenced by systematic cross-context evaluations using an 80/20 train–test split per participant and quantitative metrics (accuracy, precision, recall, and F1-score) averaged across participants. The observed variations in performance across stress scenarios, classifiers, and EEG sensors provide empirical support for this claim. Full article

Sleep is a physiological signal which plays a vital role in maintaining human health and well-being. Polysomnographic records provide insights into the various changes occurring during sleep, and hence its study is important in diagnosing various disorders including sleep disorders. As polysomnographic records encapsulate several biological signals, an extraction of EEG signals requires efficient denoising. Thus, a reliable tool for artifact removal is essential in the field of biomedical applications. The CNN is used for its feature extraction and robustness and the least mean square filter for its noise suppression. As the techniques complement one another, a combination of both leads to a better denoised EEG signal. In this approach, CNN is used for the precise removal of artifacts and then an LMS filter is used for its effective adaptation in real-time. The hybridization of both techniques in a hardware-based environment is largely. unexplored. As a result, this study proposes an integration of convolutional neural networks and least mean square filtering for an efficient denoising of EEG signals. Both techniques are optimized to tailor the design to hardware requirements. CNN is refined using the Strassen–Winograd algorithm. The Strassen–Winograd algorithm simplifies matrix multiplication, contributing to a more hardware-optimized design. In this study LMS filtering is analyzed and optimized using several optimizations. The optimizations are two’s complement distributed arithmetic algorithm, offset binary coding-based distributed arithmetic, offset binary coding Radix 4-based distributed arithmetic, as well as a Coordinate Rotation Digital Computer. The CNN with offset binary radix 4 distributed arithmetic-based LMS filter has resulted in a decrease in area of 77% and a decrease in power by 69.1%. But, in terms of Signal to Noise Ratio, Mean Squared Error and Correlation Coefficient, the CNN with offset binary coding distributed arithmetic-based LMS filter has shown better performance. The design was synthesized and implemented in Vivado 19.1. The power and area reduction in this study makes it even more suitable for wearable devices. Full article

The study is focused on the application of the electronic proof of concept Digital Twin (DT) model supporting Electroencephalogram (EEG) signal detection and interpretation. The EEG DT model integrates two open source tools: a first tool used for the circuit modeling and simulation of the electrodes, and a second one implementing an Artificial Intelligence (AI)-supervised algorithm to classify and adjust a noisy EEG signal. Specifically, the DT model adopts the Random Forest (RF) AI-supervised algorithm, replacing the signal filtering process and facilitating the time–domain peak and the wave shape morphology reading of a noisy detection. In order to prove the DT’s efficacy, the RF model is trained by considering the specific case of detections of EEG of patients under the effects of alcohol. The choice of the RF algorithm is justified by its good performance parameters. For the specific dataset, the RF exhibits a probabilistic error slightly lower than that of the ANN and a better cleaning action. The goal of the paper is to provide a methodology to use ‘intelligent’ electrodes supporting EEG data processing during data acquisition and to optimize the measurement’s interpretation through a data post-processing process. The proposed EEG DT could represent an alternative to the traditional denoising signal processing approaches. Full article

This study explores the link between the emotion “guilt” and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the study were developed to ignite guilt and neutral emotions. Two emotions, “guilt” and “neutral”, were recorded from 16 participants after these emotions were induced using storyboards as pictorial stimuli. These storyboards were developed based on various guilt-provoking events shared by another group of participants. In the pre-processing step, collected data were de-noised using bandpass filters and ICA, then segmented into smaller sections for further analysis. Two approaches were used to feed these data to the SVM classifier. First, the novel approach employed involved feeding the data to SVM classifier without computing any features. This method provided an average accuracy of 83%. In the second approach, data were divided into Alpha, Beta, Gamma, Theta and Delta frequency bands using Discrete Wavelet Decomposition. Afterward, the computed features, including entropy, Hjorth parameters and Band Power, were fed to SVM classifiers. This approach achieved an average accuracy of 63%. The findings of both classification methodologies indicate that females are more expressive in response to depicted stimuli and that their brain cells exhibit higher feature values. Moreover, females displayed higher accuracy than males in all bands except the Delta band. Full article

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. Most existing methods mainly focus on identifying the characteristics of clean EEG signals to facilitate artifact removal; however, the potential to integrate cross-disciplinary knowledge, such as insights from artifact research, remains an area that requires further exploration. In this study, we developed DHCT-GAN, a new EEG denoising model, using a dual-branch hybrid network architecture. This model independently learns features from both clean EEG signals and artifact signals, then fuses this information through an adaptive gating network to generate denoised EEG signals that accurately preserve EEG signal features while effectively removing artifacts. We evaluated DHCT-GAN’s performance through waveform analysis, power spectral density (PSD) analysis, and six performance metrics. The results demonstrate that DHCT-GAN significantly outperforms recent state-of-the-art networks in removing various artifacts. Furthermore, ablation experiments revealed that the hybrid model surpasses single-branch models in artifact removal, underscoring the crucial role of artifact knowledge constraints in improving denoising effectiveness. Full article

Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during signal acquisition and processing often distort these findings. Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction. Hence, a novel Denoised Harmonic Subtraction and Transient Temporal Feature Extraction is proposed to improve the analysis of the impact of binaural beats on anxiety levels. Initially, a novel Wiener Fused Convo Filter is introduced to capture spatial features and eliminate linear noise in EEG signals. Next, an Intrinsic Harmonic Subtraction Network is employed, utilizing the Attentive Weighted Least Mean Square (AW-LMS) algorithm to capture nonlinear summation and resonant coupling effects, effectively eliminating the misinterpretation of brain rhythms. To address the challenge of fine-grained temporal dynamics, an Embedded Transfo XL Recurrent Network is introduced to detect and extract relevant parameters associated with transient events in EEG data. Finally, EEG data undergo harmonic reduction and temporal feature extraction before classification with a cross-correlated Markov Deep Q-Network (DQN). This facilitates anxiety level classification into normal, mild, moderate, and severe categories. The model demonstrated a high accuracy of 95.6%, precision of 90%, sensitivity of 93.2%, and specificity of 96% in classifying anxiety levels, outperforming previous models. This integrated approach enhances EEG signal processing, enabling reliable anxiety classification and offering valuable insights for therapeutic interventions. Full article

Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience to diagnose neural disorders and analyse brain activity. However, ocular and myogenic artifacts from eye movements and facial muscle activity often contaminate EEG signals, compromising signal analysis accuracy. While deep learning models are a popular choice for denoising EEG signals, most focus on removing either ocular or myogenic artifacts independently. This paper introduces a novel EEG denoising model capable of handling the simultaneous occurrence of both artifacts. The model uses convolutional layers to extract spatial features and a fully connected layer to reconstruct clean signals from learned features. The model integrates the Adam optimiser, average pooling, and ReLU activation to effectively capture and restore clean EEG signals. It demonstrates superior performance, achieving low training and validation losses with a significantly reduced $RRMSE$ value of 0.35 in both the temporal and spectral domains. A high cross-correlation coefficient of 0.94 with ground-truth EEG signals confirms the model’s fidelity. Compared to the existing architectures and models (FPN, UNet, MCGUNet, LinkNet, MultiResUNet3+, Simple CNN, Complex CNN) across a range of signal-to-noise ratio values, the model shows superior performance for artifact removal. It also mitigates overfitting, underscoring its robustness in artifact suppression. Full article

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., $350\times 18$). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., $99.946\%$), Mutual Information (i.e., $98.902\%$), Independent Component Analysis (i.e., $99.62\%$), and Principal Component Analysis (i.e., $98.884\%$) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is $99.89\%$. The experiments’ findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI). Full article

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes. Full article

Supervised classification algorithms for processing epileptic EEG signals rely heavily on the label information of the data, and existing supervised methods cannot effectively solve the problem of analyzing unlabeled epileptic EEG signals. In the traditional unsupervised clustering algorithm, the number of clusters and the global parameters must be predetermined, and the algorithm’s analytical results are combined with a huge number of subjective errors, which affects the detection accuracy. For this reason, this paper proposes an unsupervised multivariate feature adaptive clustering analysis algorithm based on epileptic EEG signals. First, CEEMDAN and CWT are introduced into the epileptic EEG signal after preprocessing for joint denoising to further improve the signal quality. Then, the multivariate feature set of the signal is extracted and constructed, which includes nonlinear, time, frequency, and time-frequency characteristics. To reveal the hidden structures and correlations in the high-dimensional feature data, t-SNE dimensionality reduction is introduced. Finally, the DBSCAN clustering algorithm is optimized using the SSA algorithm to achieve adaptive selection of cluster number and global parameters.It not only enhances the clustering performance and reliability of the clustering results, but also avoids subjective errors in the analysis results. It provides a pre-theoretical foundation for the successful development of future seizure prediction devices and has good application prospects in clinical diagnosis and daily monitoring of patients. Full article

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%). Full article

An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user identification and tack classification. ESML consists of two parts: one is the $ESM{L}_1$ model via an LSTM-based method for EEG-user linking, and one is the $ESM{L}_2$ model via a CNN-based method for EEG-task linking. The new model ESML is simple, but effective and efficient. It does not require any restrictions for EEG data collection on motions and thinking for users, and it does not need any EEG preprocessing operations, such as EEG denoising and feature extraction. The experiments were conducted on three public datasets and the results show that ESML performs the best and achieves significant performance improvement when compared to baseline methods (i.e., SVM, LDA, NN, DTS, Bayesian, AdaBoost and MLP). The $ESM{L}_1$ model provided the best precision at $96\%$ with 109 users and the $ESM{L}_2$ model achieved $99\%$ precision at 3-Class task classification. These experimental results provide direct evidence that EEG signals can be used for user identification and task classification. Full article

The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing. Full article

Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. The dataset was collected using a clinical EEG system with 19 scalp electrodes while participants were in a resting state with their eyes closed. The data collection process included rigorous quality control measures to ensure data accuracy and consistency. The dataset contains recordings of 36 Alzheimer’s patients, 23 frontotemporal dementia patients, and 29 healthy age-matched subjects. For each subject, the Mini-Mental State Examination score is reported. A monopolar montage was used to collect the signals. A raw and preprocessed EEG is included in the standard BIDS format. For the preprocessed signals, established methods such as artifact subspace reconstruction and an independent component analysis have been employed for denoising. The dataset has significant reuse potential since Alzheimer’s EEG Machine Learning studies are increasing in popularity and there is a lack of publicly available EEG datasets. The resting-state EEG data can be used to explore alterations in brain activity and connectivity in these conditions, and to develop new diagnostic and treatment approaches. Additionally, the dataset can be used to compare EEG characteristics between different types of dementia, which could provide insights into the underlying mechanisms of these conditions. Full article

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models’ performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics. Full article