Prolonged exposure to mental stress is a major trigger for anxiety and depression, impairing emotional regulation and cognitive function. Given that up to 60% of patients do not benefit from conventional antidepressants, alternative therapies are urgently needed. Transcutaneous auricular vagus nerve stimulation (taVNS), a noninvasive neuromodulatory technique, has gained attention for its potential to target shared neural pathways common to both stress and depression. Advanced sensor technologies-including neural modalities such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS); autonomic modalities including electrocardiography (ECG), photoplethysmography (PPG), electrodermal activity (EDA), and electromyography (EMG); and biochemical sensors for cortisol and dopamine (DA)-enable quantitative monitoring of taVNS-induced effects. However, comprehensive reviews assessing taVNS's neuromodulatory and autonomic effects on related biomarkers remain lacking. This systematic review synthesizes findings from selected studies published between 2016 and 2025, mapping sensor-based biomarkers to clinical outcomes in stress, depression, anxiety, and emotion regulation. fMRI evidence demonstrates taVNS's therapeutic effects on depression through modulation of dysregulated reward-related neural pathways, as well as its promise in mitigating stress, reflected by improvements in autonomic biomarkers such as heart rate variability (HRV) and reductions in cortisol level. Despite these promising findings, variabilities in study designs, stimulation parameters, and outcome measures limit generalizability. Future work should focus on standardized protocols, long-term efficacy, and integrated multimodal sensing to optimize closed-loop taVNS therapy.

Mental stress is a prevalent issue in high-demand work environments, negatively impacting sleep quality and job performance. This study introduces a novel approach to detect and mitigate elevated stress levels using multimodal framework combining functional Near Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), behavioral and biochemical markers and binaural beats stimulation. The experiment was performed at the workplace across four different mental states: control, stress, stress mitigation, and after mitigation follow up. Features from EEG and fNIRS were extracted using Partial Directed Coherence (PDC) and fused via Canonical Correlation Analysis (CCA) and Joint Sparse CCA (JSCCA). Results show that the CCA analysis has identified brain regions associated with stress, mainly in the dorsolateral prefrontal cortex (DLPFC), for all phases of the experiment. Behavioral analysis demonstrated that the application of Binaural Beats (BBs) significantly reduced the reaction time (p < 0.000012) and increased the accuracy (p < 0.002) compared to the stress phase (no binaural beat). As for the binary classification of stress using Na & iuml;ve Bayes for control-stress classes, the CCA improved the accuracy (98.38%), specificity (97.94%), and sensitivity (98.82%) results compared to using single-modality PDC features from EEG and fNIRS, and outperformed JSCCA. In addition, Binaural Beats (BBs) demonstrated stress-mitigation effects, but their long-term efficacy requires further investigation. This integrated approach offers promising advancements in workplace stress detection and mitigation, potentially leading to improved occupational health and productivity.

Smiles do not always signify genuine positive feelings. Distinguishing real from fake smiles has become a critical research area, with existing methods such as facial expression analysis being susceptible to manipulation. This signals the need for manipulation-resistant modalities. Electroencephalography (EEG) is immune to such manipulations. Therefore, this study introduces a deep learning framework combining transfer learning and emotion-aware feature fusion to classify smile authenticity effectively. The proposed approach is built on the fact that smiles are interconnected with emotional states, which offer crucial contextual information for smile discrimination. The framework has three stages: first, an EEG-based emotion recognition model was developed using CNNs and topographical EEG mapping to capture spatiotemporal patterns associated with emotional states. Second, a smiles-specific CNN was designed to process EEG features tailored to smile classification. Third, transfer learning was employed to fine-tune the emotion recognition model for smile classification. To enhance feature integration, a cross-attention mechanism was applied, aligning emotion-specific and smile-specific features dynamically to capture cues of smile authenticity. The framework achieved an accuracy of 74.303

Pectin, a natural biopolymer, is a cost-effective, biocompatible, non-toxic, abundant, and flexible material, making it suitable for recording high-quality bioelectric signals from the dynamic surface of the human body. In this work, pectin-based flexible bioelectrodes were developed for the non-invasive monitoring of biopotentials. The bioelectrodes are composed of pectin, polyaniline emeraldine salt (PANI-ES), glycerol, and polydimethylsiloxane (PDMS) and therefore abbreviated as PPGP. The PPGP electrodes demonstrated a bulk electrical conductivity of (7.54 ± 0.81) × 10−3 S/cm, a very low impedance of 34 Ω, and a high charge storage capacity of 4.63 ± 2.70 mC/cm2. The surface morphology of the PPGP electrode plays a crucial role in enhancing biopotential signal detection by improving adhesion to skin contours. PPGP electrodes have been successfully used for high-fidelity electromyographic (EMG) bioelectric signal measurements. The developed PPGP bioelectrodes have the potential to advance next-generation human–machine interface (HMI) technologies and wearable healthcare systems, including prosthetic control, rehabilitation monitoring, and assistive communication devices.

This study addresses limitations in EEG-based stress detection research by developing a novel approach to differentiate multiple mental states in different stress baseline population samples. Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). The experiment comprises four phases: rest state, control alertness, stress induction, and stress mitigation. Mental states were assessed using behavioral data: reaction time to stimuli (RT) and target detection accuracy, subjective reports: Perceived Stress Scale scores (PSS-10), biochemical indicators: salivary cortisol levels, and neurophysiological measure: EEG effective connectivity via Partial Directed Coherence (PDC). BBs significantly improved target detection accuracy by 31.6% and 22.8% for low and high-stress groups, respectively. PDC connectivity showed a shift to the temporal region during mitigation, indicating a return to a more balanced state. GCN classification achieved accuracies of 76.43 +/- 9.01 % and 76.32 +/- 7.79 % for each group, and 76.37 +/- 8.40 % for a common baseline. While 16-Hz BBs enhanced focusing abilities they did not significantly reduce subjective stress scores. This study highlights the complex relationship between cognitive performance, perceived stress, and neurophysiological measures, emphasizing the need for multifaceted stress research and management approaches.

Visual imagery (VI) is a mental process in which an individual generates and sustains a mental image of an object without physically seeing it. Recent advancements in assistive technology have enabled the utilization of VI mental tasks as a control paradigm to design brain–computer interfaces (BCIs) capable of generating numerous control signals. This, in turn, enables the design of control systems to assist individuals with locked-in syndrome in communicating and interacting with their environment. This paper presents an electroencephalogram (EEG) dataset captured from 30 healthy native Arabic-speaking subjects (12 females and 18 males; mean age: 20.8 years; age range: 19–23) while they visually imagined the 28 letters of the Arabic alphabet. Each subject conducted 10 trials per letter, resulting in 280 trials per participant and a total of 8400 trials for the entire dataset. The EEG signals were recorded using the EMOTIV Epoc X wireless EEG headset (San Francisco, CA, USA), which is equipped with 14 data electrodes and two reference electrodes arranged according to the 10–20 international system, with a sampling rate of 256 Hz. To the best of our knowledge, this is the first EEG dataset that focuses on visually imagined Arabic letters.

Classifying mental states is important for identifying the type and severity of stress, in order to guide appropriate treatment. In this study, we utilized electroencephalography (EEG) signals and convolutional neural networks (CNNs) to classify four different mental states: rest, controlled alertness, stress, and stress mitigation. The control alert and stress states were induced using the Stroop color-word test (SCWT), both with and without time constraints. Stress mitigation was achieved through the use of 16-Hz binaural beat (BB) stimulation. We quantified the four mental states using target detection accuracy, subjective scores, EEG functional connectivity network images estimated by phase locking value (PLV), and CNNs. Our results showed that solving SCWT under time constraints reduced the target detection accuracy by 70% ( p<0.00001 ), while BBs improved the accuracy of detection by 28% ( p=0.00470 ). The functional connectivity networks showed significant differences ( p<0.05 ) between frontal/occipital and parietal regions across the four mental states. The CNNs classified the four mental states with 82.96% accuracy, 83.4% sensitivity, 94.96% specificity, 84.67% precision, and 84.03% F-score, with optimal performance at the beta band. Other EEG bands produced classification accuracies below 68%. Overall, the results indicate that 16-Hz BBS can effectively mitigate stress levels, and CNNs with beta EEG-PLV images show promise for classifying four mental states. The results of this study provide a foundation for implementing stress management interventions in order to maximize eustress and minimize distress in the workplace.

Flexible and biocompatible electrodes are crucial components in developing future wearable and implantable biomedical devices.

This systematic review provides a comprehensive evaluation of current and emerging methodologies in magnetoencephalography/electroencephalography (M/EEG) source localization by addressing critical technological and methodological gaps. The review follows systematic review and meta-analysis (PRISMA) guidelines and meticulously examines a wide range of source localization methods, making it the most exhaustive evaluation in the field. It categorizes 28 distinct methods into five core groups: basic, hybrid, subspace-based, probabilistic, and machine learning-based, and offers an in-depth comparative analysis. Our findings reveal a pressing need for advancements in accuracy, spatial-temporal resolution, and computational efficiency. We identify the imperative for improved signal processing, advanced modeling, integration of machine learning, and AI, to enhance source localization accuracy. The review advocates for personalized, real-time applications in clinical settings and underscores the importance of multimodal neuroimaging studies for comprehensive brain activity insights. We recommend longitudinal, large-scale studies and open science practices for validating and generalizing findings. This review stands as a definitive guide for future research, aiming to propel M/EEG source localization to new heights of accuracy and clinical utility.

Individuals experiencing high levels of stress face significant impacts on their overall well-being and quality of life. Electrical stimulation techniques have emerged as promising interventions to address mental stress, depression, and anxiety. This systematic review investigates the impact of different electrical stimulation approaches on these types of disorders. The review synthesizes data from 30 studies, revealing promising findings and identifying several research gaps and challenges. The results indicate that electrical stimulation has the potential to alleviate symptoms of anxiety, depression, and tension, although the degree of efficacy varies among different patient populations and modalities. Nevertheless, the findings also underscore the necessity of standardized protocols and additional research to ascertain the most effective treatment parameters. There is also a need for integrated methodologies that combine hybrid EEG-fNIRS techniques with stress induction paradigms, the exploration of alternative stimulation modalities beyond tDCS, and the investigation of the combined effects of stimulation on stress. Despite these challenges, the growing body of evidence underscores the potential of electrical stimulation as a valuable tool to manage mental stress, depression, and anxiety, paving the way for future advancements in this field.

Sample size calculation is crucial in biomedical in vivo research investigations mainly for two reasons: to design the most resource-efficient studies and to safeguard ethical issues when alive animals are subjects of testing. In this context, power analysis has been widely applied to compute the sample size by predetermining the desired statistical power and the significance level. To verify whether the assumption of a null hypothesis is true, repeated measures analysis of variance (ANOVA) is used to test the differences between multiple experimental groups and control group(s). In this article, we focus on the a priori power analysis, for testing multiple parameters and calculating the power of experimental designs, which is suitable to compute the sample size of trial groups in repeated measures ANOVA. We first describe repeated measures ANOVA and the statistical power from a practical aspect of biomedical research. Furthermore, we apply the G*Power software to conduct the a priori power analysis using examples of repeated measures ANOVA with three groups and five time points. We aim not to use the typical technically adapted statistical language. This will enable experimentalists to confidently formulate power calculation and sample size calculation easier and more accurately.

This research explores the positive application of deepfake technology for upper body generation, specifically sign language for the D(d)eaf and hard of hearing (DHoH) community. Given the complexity of sign language and the scarcity of experts, the generated videos are vetted by a sign language expert for accuracy. We construct a reliable deepfake dataset, evaluating its technical and visual credibility using computer vision and natural language processing models. The dataset, consisting of over 1200 videos featuring both seen and unseen individuals to the generation model, is also used to detect deepfake videos targeting vulnerable individuals. Expert annotations confirm that the generated videos are comparable to real sign language content. Linguistic analysis, using textual similarity scores and interpreter evaluations, shows that the interpretation of generated videos is at least 90% similar to authentic sign language. Visual analysis demonstrates that convincingly realistic deepfakes can be produced, even for new subjects. Using a pose/style transfer model, we pay close attention to detail, ensuring hand movements are accurate and align with the driving video. We also apply machine learning algorithms to establish a baseline for deepfake detection on this dataset, contributing to the detection of fraudulent sign language videos.

This study examines the impact of transcranial alternating current stimulation (tACS) on the initial dip; an initial decrease in oxygenated hemoglobin (HbO) that arises during early neural activation, in individuals experiencing mental stress. Using functional near-infrared spectroscopy (fNIRS), we examined the effects of tACS on the initial dip and prefrontal activation in 40 participants subjected to mental stress induced by the Stroop Color-Word Task. The results indicate that tACS maintains the amplitude of the initial dip following stimulation, showing no significant change in amplitude (p > 0.05). In contrast, the sham group demonstrated a significant increase in initial dip amplitude (p < 0.05). This indicates that tACS improves neural efficiency by sustaining initial metabolic responses. Moreover, tACS-induced changes in functional connectivity demonstrated a reorganization of brain activity, characterized by a significant reduction in task-based connectivity (p < 0.001), which may enhance cognitive performance. Behavioral and physiological measures indicated a 15% reduction in salivary alpha amylase levels and a 20% improvement in NASA Task Load Index scores, thereby supporting the stress-mitigating effects of tACS. Machine learning classifiers demonstrated significant accuracy in differentiating brain states, achieving classification accuracies as high as 92.6% for the initial dip phase utilizing convolutional neural network. The findings underscore the therapeutic potential of tACS in modulating early neural responses to stress, providing new insights into non-invasive modulation for cognitive performance and mental well-being.

This pilot study investigates the impact of transcranial alternating current stimulation (tACS) on psychological stress using functional near-infrared spectroscopy (fNIRS). Forty volunteers were randomly assigned to two groups: the tACS and the control. The experiment was divided into three distinct stages: pre-stimulation, stimulation, and post-stimulation. The Stroop Color-Word Task (SCWT) was employed as a validated stress-inducing paradigm to assess pre- and post-stimulation changes. During the initial phase, the participants completed the SCWT. This was followed by either tACS or sham. In the third session, the individuals solved the task again. The anode and cathode for the transcranial tACS were placed on the dorsolateral prefrontal cortex (DLPFC). tACS, was applied with current intensity of 1.5 mA at 16 Hz over the dorsolateral prefrontal cortex (DLPFC), aimed to modulate cortical activation and mitigate stress. Sham included 5-second ramp periods. Physiological data using alpha amylase and the NASA Task Load Index (NASA-TLX) were utilized. The results revealed significant hemodynamic changes and reduced stress levels in the tACS group compared to the sham group (p < 0.001). The connectivity network changed significantly (p < 0.001) following tACS. In addition, the NASA-TLX results showed a statistically significant difference between the pre-and post-tACS sessions. In contrary, no statistical significance was noticed for the sham control group. An increase in the blood flow in the prefrontal cortex region of the brain was observed, demonstrating the potential of tACS as a non-invasive neuromodulation technique for stress mitigation.

Deepfake technology, powered by generative models such as GANs and Diffusion Models, has surged in popularity, producing synthetic facial content that is increasingly difficult to distinguish from the real media. Current state-of-the-art deepfake detectors, often based on Vision Transformers and Convolutional Neural Networks, perform well under clean conditions. However, their robustness under adversarial perturbations can be questionable. In this paper, we evaluate the robustness of eight pre-trained models across sixteen lowcost and easy-to-implement adversarial attacks. The attacks include various noise types (e.g., Gaussian, salt-and-pepper, Poisson) of different levels, affine transformations, cropping, and their combinations. We assess detection performance across three categories of facial content: Real, Fake (manipulated real faces), and Synthetic (AI-generated faces with no real counterpart). We found that top performing models excel on clean data but falter significantly under noise perturbations, particularly for Synthetic images, which exhibit the largest relative accuracy drops (e.g., 50.65% for combined noise scenario). In contrast, simpler models show greater resilience or even improvements in certain cases, such as vertical cropping (e.g., -6.39 % for Synthetic). These results expose a critical gap in current detection approaches, especially for Synthetic face images, and urge the development of more resilient systems to tackle adversarial threats effectively.

Stress is a major global health issue linked to various physical illnesses and psychological disturbances. Hence, effective stress mitigation strategies are crucial for maintaining public health. This study investigates the gender-specific biochemical and neural effects of transcranial alternating current stimulation (tACS) using salivary biomarkers and Electroencephalography (EEG)-based machine learning classification. EEG, salivary cortisol, and alpha-amylase (sAA) levels were measured across four experimental phases in both sham and tACS groups. Results show a stronger cortisol response to cognitive stress triggered by Stroop Color-Word Test (SCWT) in female participants ($\mathbf{p}$-value = $\mathbf{0. 1}$), who also exhibited faster recovery following tACS ($p$-value 0.015). In contrast, male participants showed minimal cortisol fluctuations (all $\mathbf{p}$-values above 0.8). Both male and female subjects experienced a reduced sAA levels post-tACS ($\mathbf{p}$-value = $\mathbf{0. 0 6}$), indicating a possible stress-mitigation effect. Among five machine learning algorithms tested on the recorded EEG data, the k-nearest neighbors (KNN) achieved the highest classification accuracy (57.66 % to 99.23) within the beta frequency band. Classification performance improved significantly in males following tACS. In contrast, sham female subjects showed marked variability and classification accuracy. These findings highlight the beta band's sensitivity to tACS-induced neural modulation and underscore the sex-specific differences in stress responses and neuroplasticity.

Electrophysiological (EP) disorders are serious and life-threatening conditions but their prevention can be improved with advanced on-skin and implantable biomedical sensors. These sensors are expected to shift rapidly from the development stage to the commercial stage, offering next generation and more sustainable life-saving approaches. They have the potential to advance medical diagnostics and healthcare. However, current bioelectronic sensors face major challenges related to their biocompatibility, long-term monitoring, reliability, low signal-to-noise ratio (SNR), stability, and wireless data transmission. Addressing these issues requires a multidisciplinary approach that integrates bioengineering, neuroengineering, and data science. This review article covers the most recent developments in materials and methods for on-skin, flexible, and implantable biomedical sensors designed for biosignal monitoring. Advanced bioelectronics, such as electronic skin (e-skin), patches, electronic tattoos, headbands, smart textiles, and microneedles for biopotential monitoring are discussed. The review also identifies the key gaps in the current technologies and strategies to address these gaps. Future research directions for sustainable life-saving applications are proposed. Furthermore, the practical use of bioelectric signal-based human-machine interfaces (HMIs) for controlling devices such as home appliances and robots is also discussed, showcasing the potential of these technologies to enhance everyday life. This review inspires researchers to explore innovative solutions advancing the field toward next-generation sustainable life-saving applications.

Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation.

BACKGROUND:SCI is a time-sensitive debilitating neurological condition without treatment options. Although the central nervous system is not programmed for effective endogenous repairs or regeneration, neuroplasticity partially compensates for the dysfunction consequences of SCI.OBJECTIVE AND HYPOTHESIS:The purpose of our study is to investigate whether early induction of hypothermia impacts neuronal tissue compensatory mechanisms. Our hypothesis is that although neuroplasticity happens within the neuropathways, both above (forelimbs) and below (hindlimbs) the site of spinal cord injury (SCI), hypothermia further influences the upper limbs' SSEP signals, even when the SCI is mid-thoracic.STUDY DESIGN:A total of 30 male and female adult rats are randomly assigned to four groups (n = 7): sham group, control group undergoing only laminectomy, injury group with normothermia (37°C), and injury group with hypothermia (32°C +/-0.5°C).METHODS:The NYU-Impactor is used to induce mid-thoracic (T8) moderate (12.5 mm) midline contusive injury in rats. Somatosensory evoked potential (SSEP) is an objective and non-invasive procedure to assess the functionality of selective neuropathways. SSEP monitoring of baseline, and on days 4 and 7 post-SCI are performed.RESULTS:Statistical analysis shows that there are significant differences between the SSEP signal amplitudes recorded when stimulating either forelimb in the group of rats with normothermia compared to the rats treated with 2h of hypothermia on day 4 (left forelimb, p = 0.0417 and right forelimb, p = 0.0012) and on day 7 (left forelimb, p = 0.0332 and right forelimb, p = 0.0133) post-SCI.CONCLUSION:Our results show that the forelimbs SSEP signals from the two groups of injuries with and without hypothermia have statistically significant differences on days 4 and 7. This indicates the neuroprotective effect of early hypothermia and its influences on stimulating further the neuroplasticity within the upper limbs neural network post-SCI. Timely detection of neuroplasticity and identifying the endogenous and exogenous factors have clinical applications in planning a more effective rehabilitation and functional electrical stimulation (FES) interventions in SCI patients.

Conventional surface electrodes are composed of rigid metals such as Ag/AgCl that are not only harsh to the skin but also irritating if used as wet electrodes. Furthermore, rigid, inflexible surface electrodes can cause patient discomfort when used for the long term. To reduce the mechanical mismatch, flexible alternatives to metal electrodes are needed. This study reports the development of highly flexible composite electrodes fabricated from the conductive dopant boronic acid-modified carbon dots embedded in a polydimethylsiloxane matrix. The electrodes were characterized for their structural, electrochemical, and mechanical characteristics and for their ability to record electrophysiological signals. Furthermore, the composition of these electrodes was varied systematically to obtain the optimal electrochemical and mechanical properties. The best-performing electrode, composed of 10% boronic acid-modified carbon dots, 16% glycerol, and 74% polydimethylsiloxane (8:1 elastomer to curing agent), had a smooth surface, a promising conductivity of 9.62 x 10(-3) S/cm, an impedance of 964 k Omega at 1 kHz, and a charge storage capacity of 21.4 mu C/cm(2). This electrode had a Young's modulus of 0.0545 MPa, which is compatible with biological tissues' elasticity. The fabricated electrodes recorded high-quality electrocardiography signals with a promising signal-to-noise ratio (SNR) of 36.75 dB that is comparable to that of commercial Ag/AgCl, which had an SNR of 39.98 dB. A similarly good performance was observed with electromyography. Furthermore, the developed flexible surface electrodes maintained their ability to record high-quality ECG and EMG over a period of 3 weeks.