Search Results (142)

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

Data analysis and machine learning have become essential cross-disciplinary skills for engineering students and professionals. Traditionally, these topics are taught through lectures or online courses using pre-existing datasets, which limits the opportunity to engage with the full cycle of data analysis and machine learning, including data collection, preparation, and contextualization of the application field. To address this, we designed and implemented a learning activity that involves students in every step of the learning process. This activity includes multiple stages where students conduct experiments to record their own electroencephalographic (EEG) signals and use these signals to learn data analysis and machine learning techniques. The purpose is to actively involve students, making them active participants in their learning process. This activity was implemented in six courses across four engineering careers during the 2023 and 2024 academic years. To validate its effectiveness, we measured improvements in grades and self-reported motivation using the MUSIC model inventory. The results indicate a positive development of competencies and high levels of motivation and appreciation among students for the concepts of data analysis and machine learning. Full article

Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making. Full article

(1) Background: Biofeedback and neurofeedback are gaining attention as non-invasive rehabilitation strategies in Parkinson’s disease (PD) treatment, aiming to modulate motor and non-motor symptoms through the self-regulation of physiological signals. (2) Objective: This review explores the application of biofeedback techniques, electromyographic (EMG) biofeedback, heart rate variability (HRV) biofeedback, and electroencephalographic (EEG) neurofeedback in PD rehabilitation, analyzing their impacts on motor control, autonomic function, and cognitive performance. (3) Methods: This review critically examined 15 studies investigating the efficacy of electromyographic (EMG), heart rate variability (HRV), and electroencephalographic (EEG) feedback interventions in PD. Studies were selected through a systematic search of peer-reviewed literature and analyzed in terms of design, sample characteristics, feedback modality, outcomes, and clinical feasibility. (4) Results: EMG biofeedback demonstrated improvements in muscle activation, gait, postural stability, and dysphagia management. HRV biofeedback showed positive effects on autonomic regulation, emotional control, and cardiovascular stability. EEG neurofeedback targeted abnormal cortical oscillations, such as beta-band overactivity and reduced frontal theta, and was associated with improvements in motor initiation, executive functioning, and cognitive flexibility. However, the reviewed studies were heterogeneous in design and outcome measures, limiting generalizability. Subgroup trends suggested modality-specific benefits across motor, autonomic, and cognitive domains. (5) Conclusions: While EMG and HRV systems are more accessible for clinical or home-based use, EEG neurofeedback remains technically demanding. Standardization of protocols and further randomized controlled trials are needed. Future directions include AI-driven personalization, wearable technologies, and multimodal integration to enhance accessibility and long-term adherence. Biofeedback presents a promising adjunct to conventional PD therapies, supporting personalized, patient-centered rehabilitation models. Full article

Background/Objectives: We present a novel approach for detecting generalized sleep pathologies through the fractal analysis of single-channel electroencephalographic (EEG) signals. We propose that the fractal scaling exponent of permutation entropy time series serves as a robust biomarker of pathological sleep patterns, capturing alterations in brain dynamics across multiple disorders. Methods: Using two public datasets (Sleep-EDF and CAP Sleep Database) comprising 200 subjects (112 healthy controls and 88 patients with various sleep pathologies), we computed the fractal scaling of the permutation entropy of these signals. Results: The results demonstrate significantly reduced scaling exponents in pathological sleep compared to healthy controls (mean = 1.24 vs. 1.06, $p<0.001$), indicating disrupted long-range temporal correlations in neural activity. The method achieved 90% classification accuracy for rapid-eye-movement (REM) sleep behavior disorder (F1-score: 0.89) and maintained 74% accuracy when aggregating all pathologies (insomnia, narcolepsy, sleep-disordered breathing, etc.). Conclusions: The advantages of this approach, including compatibility with single-channel EEG (enabling potential wearable applications), independence from sleep-stage annotations, and generalizability across recording montages and sampling rates, stablish a framework for non-specific sleep pathology detection. This is a computationally efficient method that could transform screening protocols and enable earlier intervention. The robustness of this biomarker could enable straightforward clinical applications for common sleep pathologies as well as diseases associated with neurodegenerative conditions. Full article

The development of materials science, artificial intelligence and wearable technology has created both opportunities and challenges for the next generation of bionic sensor technology. Bionic sensors are extensively utilized in the collection and monitoring of human biological signals. Human biological signals refer to the parameters generated inside or outside the human body to transmit information. In a broad sense, they include bioelectrical signals, biomechanical information, biomolecules, and chemical molecules. This paper systematically reviews recent advances in bionic sensors in the field of biometric acquisition and monitoring, focusing on four major technical directions: bioelectric signal sensors (electrocardiograph (ECG), electroencephalograph (EEG), electromyography (EMG)), biomarker sensors (small molecules, large molecules, and complex-state biomarkers), biomechanical sensors, and multimodal integrated sensors. These breakthroughs have driven innovations in medical diagnosis, human–computer interaction, wearable devices, and other fields. This article provides an overview of the above biomimetic sensors and outlines the future development trends in this field. Full article

Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies. Full article

Objective: Clinically, patients in a coma after cardiac arrest are given the prognosis of “neurological recovery” to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma. Approach: Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. In addition, Shapley additive explanation (SHAP) values were utilized to rank and analyze the importance of the features. Results: Our results indicated that the three different EEG features helped achieve a fivefold cross-validation receiver-operating characteristic (ROC) of 0.87. Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. Among these, low-frequency long-distance functional connectivity (45%) was associated with a poor prognosis, whereas high-frequency short-distance functional connectivity (25%) was linked with a good prognosis. Burst suppression ratio is 20%, concentrated in the left frontal–temporal and right occipital–temporal regions at high thresholds (10/15 mV), demonstrating its strong discriminative power. Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. These findings provide a clinically actionable framework for advancing neurological prognosis and optimizing patient care. Full article

Based on some typical complex network analysis methods and machine learning techniques, a general multiple-measures composited strategy-guided epilepsy diagnosis analysis framework is proposed in this brief paper. Five typical network analysis methods for biology time series analysis are utilized for real applications, including the classical visibility graph (VG), horizontal visibility graph (HVG), the limited penetrable visibility graph (LPVG), the modified frequency degree method (MFDM), and the quantity graph (QG). By using the aforementioned typical transformation methods, the EEG signal sets to be classified are transferred into graph network object sets. The main network features and related indicators are calculated and extracted as features for classification tasks. Some key features are selected via variance analysis, and the eXtreme Gradient Boosting (XGBOOST) machine learning algorithm is used for related binary and five-class classification tasks for electroencephalographic time series. Numerical experiments demonstrate that, through ten-fold cross-validation on the entire dataset, the classification accuracy for two-class classification consistently reaches 97.8% (with a specificity of 97.5%), while for five-class classification, the accuracy stably reaches 82.4% (with a specificity of 95.6%). Therefore, our classification framework can be effectively used to assist hospital doctors and medical specialists in diagnosing related diseases, especially to help accelerate the treatment of epilepsy patients. Full article

Background: Natural nootropic compounds are evidenced to restore brain function in clinical and older populations and are purported to enhance cognitive abilities in healthy cohorts. This study aimed to provide neurocomputational insight into the discrepancies between the remarkable self-reports and growing interest in nootropics among healthy adults and the inconclusive performance-enhancing effects found in the literature. Methods: Towards this end, we devised a randomised, double-blinded, and placebo-controlled study where participants performed a visual categorisation task prior to and following 60 days of supplementation with a plant-based nootropic, while electroencephalographic (EEG) signals were concurrently captured. Results: We found that although no improvements in choice accuracy or reaction times were observed, the application of multivariate information-theoretic measures to the EEG source space showed broadband increases in similar and complementary interdependencies across brain networks of various spatial scales. These changes not only resulted in localised increases in the redundancy among brain network interactions but also more significant and widespread increases in synergy, especially within the delta frequency band. Conclusions: Our findings suggest that natural nootropics can improve overall brain network cohesion and energetic efficiency, computationally demonstrating the beneficial effects of natural nootropics on brain health. However, these effects could not be related to enhanced rapid perceptual decision-making performance in a healthy adult sample. Future research investigating these specific compounds as cognitive enhancers in healthy populations should focus on complex cognition in deliberative tasks (e.g., creativity, learning) and over longer supplementation durations. Clinical trials registration number: NCT06689644. Full article

A serious game combines concepts, principles, and methods of game design with information and communication technologies for the achievement of a given goal beyond entertainment. Serious game studies have been reported under a brain–computer interface (BCI) approach, with the specific use of electroencephalographic (EEG) signals. This study presents a review of the technological solutions from existing works related to serious games and EEG signals. A taxonomy is proposed for the classification of the research literature in three different categories according to the experimental strategy for the integration of the game and EEG: (1) evoked signals, (2) spontaneous signals, and (3) hybrid signals. Some details and additional aspects of the studies are also reviewed. The analysis involves factors such as platforms and development languages (serious game), software tools (integration between serious game and EEG signals), and the number of test subjects. The findings indicate that 50% of the identified studies use spontaneous signals as the experimental strategy. Based on the definition, categorization, and state of the art, the main research challenges and future directions for this class of technological solutions are discussed. Full article

Hemispheric Asymmetry Reduction in Old Adults (HAROLD) is one of the most well-known models of compensatory brain involvement in older adults. Most evidence supports its occurrence from the perspective of PET and fMRI studies, with a deficiency in electroencephalographic research in this domain. Therefore, we aimed to investigate the possibility of identifying the HAROLD pattern in older adults’ power of theta and alpha. The study sample comprised 50 older adults and 60 young adults performing n-back tasks while recording EEG signals. The level of cognitive performance and the theta and alpha power for pairs of symmetrical electrodes in the prefrontal, frontal, and parietal areas were analyzed. Older adults exhibited inferior cognitive performance compared to young adults and heightened theta power in the right hemisphere within the prefrontal and parietal areas. However, they also demonstrated increased alpha power in the right frontal pole, which contradicts the compensatory effects of theta power. Moreover, the two indicated phenomena of lateralization of theta and alpha power in older adults were unrelated to individual cognitive performance. The results make it challenging to discern whether the revealed age-related differences in theta and alpha power lateralization denote compensation, dedifferentiation, or nonselective recruitment as neutral features of brain activity in old adults. Full article

This article presents the development of a serious game designed to help individuals improve their ability to relax and self-regulate, with a particular focus on children. Additionally, the game has the potential to become an effective tool for intervention in individuals with Attention Deficit Hyperactivity Disorder (ADHD) due to its integration of critical elements for measuring attention levels. These include omission errors, commission errors, response times, standard deviations of response times, and other relevant variables. The game allows control through electroencephalographic (EEG) signals, using alpha wave modulation and blinking as interaction methods. The amplification of alpha wave amplitude is associated with states of relaxation and mental tranquility, indicating that their modulation could potentially mitigate anxiety and enhance emotional self-regulation. The game’s primary objective is to encourage participants to attain relaxing mental states by overcoming challenges as they progress. In order to achieve this, the game’s development necessitated a comprehensive understanding of EEG signal processing, a crucial aspect meticulously explored in this article. In addition, this paper presents the results of alpha wave and flicker detection, along with a performance analysis that demonstrates satisfactory results. Subsequently, the game was assessed with children to evaluate its effectiveness, facilitating a comprehensive analysis of various performance parameters. The findings indicate that the game facilitates the gradual improvement of participants’ skills with each iteration, notably enhancing their capacity to achieve a state of relaxation. Full article