Search Results (218)

Electroencephalography signal generation remains a challenging task due to its non-stationarity, multi-scale oscillations, and strong spatiotemporal coupling. Conventional generative models, including VAEs and GAN variants such as DCGAN, WGAN, and WGAN-GP, often yield blurred waveforms, unstable spectral distributions, or lack semantic controllability, limiting their effectiveness in emotion-related applications. To address these challenges, this research proposes a Transformer-based conditional variational autoencoder–generative adversarial network (Trans-cVAE-GAN) that combines Transformer-driven temporal modeling, label-conditioned latent inference, and adversarial learning. A multi-dimensional structural loss further constrains generation by preserving temporal correlation, frequency-domain consistency, and statistical distribution. Experiments on three SEED-family datasets—SEED, SEED-FRA, and SEED-GER—demonstrate high similarity to real EEG, with representative mean ± SD correlations of Pearson ≈ 0.84 ± 0.08/0.74 ± 0.12/0.84 ± 0.07 and Spearman ≈ 0.82 ± 0.07/0.72 ± 0.12/0.83 ± 0.08, together with low spectral divergence (KL ≈ 0.39 ± 0.15/0.41 ± 0.20/0.37 ± 0.18). Comparative analyses show consistent gains over classical GAN baselines, while ablations verify the indispensable roles of the Transformer encoder, label conditioning, and cVAE module. In downstream emotion recognition, augmentation with generated EEG raises accuracy from 86.9% to 91.8% on SEED (with analogous gains on SEED-FRA and SEED-GER), underscoring enhanced generalization and robustness. These results confirm that the proposed approach simultaneously ensures fidelity, stability, and controllability across cohorts, offering a scalable solution for affective computing and brain–computer interface applications. Full article

Background: Upright position recovery (i.e., verticalization) is crucial in the rehabilitation of severe acquired brain injury (sABI). VErticalization by tilt table equipped with robotic-assisted lower limbs cyclic Mobilization (VEM) may facilitate a safer adaptation to vertical posture, reducing orthostatic hypotension occurrence. This multicenter randomized controlled trial (RCT) aims at investigating efficacy, safety, and usability of VEM compared to Traditional Verticalization (TV) using a conventional tilt table in cognitive-motor rehabilitation of sABI patients; Methods: a total of 118 sABI patients with or emerged from prolonged Disorder of Consciousness (pDoC and eDoC) will be enrolled in six post-acute Neurorehabilitation Units and randomly allocated to VEM or TV arm (for each arm: total 25 sessions of 30 min daily treatment/5 days/week/5 weeks). Patients will undergo clinical–functional assessment, resting EEG recording and blood sampling, before, at the end of treatment, and after 1 month; Results: we will expect possible differences in safety and usability of verticalization between VEM and TV rehabilitative intervention and in their efficacy to improve clinical–functional findings and brain indices; Conclusions: this RCT will provide new insights for the intensive, tailored and safe neurorehabilitation intervention in patients with sABI. Full article

Urbanization exacerbates mental health challenges, prompting the exploration of biophilic design solutions. This study examined the therapeutic potential of raw earth through its thermal interactions in architecture. First, energy consumption simulations established distinct indoor temperature ranges for raw earth, concrete, and steel under identical energy constraints: low (22.8 ± 0.32 °C), medium (26.5 ± 0.39 °C), and high (30.1 ± 0.84 °C). The study then quantified the differences in physical and psychological perceptions across material-dominated spaces under controlled temperatures above. Nine scenes were constructed for emotional healing evaluation, incorporating the olfactory dimension into the Electroencephalogram (EEG) + Virtual reality (VR) + Laboratory environmental control (LEC) approach. The results indicated that raw earth materials were most effective in promoting emotional recovery under thermal stress conditions (low/high temperatures), as evidenced by a significant enhancement of α EEG rhythms. However, under moderate conditions, concrete environments produced the greatest relaxation effects, while steel environments were most conducive to enhancing focus. The core conclusion of this study is that the therapeutic effects of building materials are not static but are intricately linked to the surrounding thermal environment. This provides a new perspective for evidence-based healthy building design and underscores the importance of optimizing material selection based on specific environmental conditions and needs. Full article

The aim of this study is to analyze the signal generated in the brain for a specific motor task and to identify the region where it occurs. For this purpose, electroencephalography (EEG) signals were divided into delta, theta, alpha, and beta frequency sub-bands, and feature extraction was performed by looking at the time-frequency characteristics of the signals belonging to the obtained sub-bands. The epoch corresponding to motor imagery or action and the signal source in the brain were determined by power spectral density features. This study focused on a hand open–close motor task as an example. A machine learning structure was used for signal recognition and classification. The highest accuracy of 92.9% was obtained with the neural network in relation to signal recognition and action realization. In addition to the classification framework, this study also incorporated advanced preprocessing and energy analysis techniques. Eye blink artifacts were automatically detected and removed using independent component analysis (ICA), enabling more reliable spectral estimation. Furthermore, a detailed channel-based and sub-band energy analysis was performed using fast Fourier transform (FFT) and power spectral density (PSD) estimation. The results revealed that frontal electrodes, particularly Fp1 and AF7, exhibited dominant energy patterns during both real and imagined motor tasks. Delta band activity was found to be most pronounced during rest with T1 and T2, while higher-frequency bands, especially beta, showed increased activity during motor imagery, indicating cognitive and motor planning processes. Although 30 s epochs were initially used, event-based selection was applied within each epoch to mark short task-related intervals, ensuring methodological consistency with the 2–4 s windows commonly emphasized in the literature. After artifact removal, motor activity typically associated with the C3 region was also observed with greater intensity over the frontal electrode sites Fp1, Fp2, AF7, and AF8, demonstrating hemispheric symmetry. The delta band power was found to be higher than that of other frequency bands across T0, T1, and T2 conditions. However, a marked decrease in delta power was observed from T0 to T1 and T2. In contrast, beta band power increased by approximately 20% from T0 to T2, with a similar pattern also evident in gamma band activity. These changes indicate cognitive and motor planning processes. The novelty of this study lies in identifying the electrode that exhibits the strongest signal characteristics for a specific motor activity among 64-channel EEG recordings and subsequently achieving high-performance classification of the corresponding motor activity. Full article

Background: Intuitive inquiry meditation (Can-Hua-Tou) is a unique mental practice which differs from relaxation-based practices by continuously demanding intuitive inquiry. It emphasizes the doubt-driven self-interrogation, also referred to as Chan/Zen meditation. Nonetheless, its electrophysiological signature remains poorly characterized. Methods: We recorded 128-channel EEG from 20 male Buddhist monks (5–28 years Can-Hua-Tou experience) and 18 male novice lay practitioners (<0.5 year) during three counter-balanced eyes-closed blocks: Zen inquiry meditation (ZEN), a phonological control task silently murmuring “A-B-C-D” (ABCD), and passive resting state (REST). Power spectral density was computed for alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–45 Hz) bands and mapped across the scalp. Mixed-design ANOVAs and electrode-wise tests were corrected with false discovery rate (p < 0.05). Results: Alpha power increased globally with eyes closed, but condition- or group-specific effects did not survive FDR correction, indicating comparable relaxation in both cohorts. In contrast, monks displayed a robust beta augmentation, showing significantly higher beta over parietal-occipital leads than novices across all conditions. The most pronounced difference lay in the gamma band: monks exhibited trait-like fronto-parietal gamma elevations in all three conditions, with additional, though sub-threshold, increases during ZEN. Novices showed negligible beta or gamma modulation across tasks. No significant group × condition interaction emerged after correction, yet only experts expressed concurrent beta/gamma amplification during meditative inquiry. Conclusions: Long-term Can-Hua-Tou practice is associated with frequency-specific neural adaptations—stable high-frequency synchrony and state-dependent beta enhancement—consistent with Buddhist constructs of citta-ekāgratā (one-pointed concentration) and vigilance during self-inquiry. Unlike mindfulness styles that accentuate alpha/theta, Chan inquiry manifests an oscillatory profile dominated by beta–gamma dynamics, underscoring that different contemplative strategies sculpt distinct neurophysiological phenotypes. These findings advance contemplative neuroscience by linking intensive cognitive meditation to enduring high-frequency cortical synchrony. Future research integrating cross-frequency coupling analyses, source localization, and behavioral correlates of insight will further fully delineate the mechanisms underpinning this advanced contemplative expertise. Full article

Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain–computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes—right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject’s evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb. Full article

Human cognitive performance is influenced by external factors, including indoor environmental quality (IEQ). Understanding how these factors affect stress, attention, and relaxation is essential in environments such as workplaces and educational institutions, where cognitive function directly impacts performance. This study examines the effects of IEQ on students’ attention and relaxation levels during various lecture periods, focusing on design major students. Three key IEQ parameters (air temperature, relative humidity, and natural lighting) were evaluated for their effects on cognitive states using electroencephalogram (EEG) measurements in a controlled setting. Participants wore non-invasive, portable EEG devices to monitor neurophysiological activity across two sessions, each involving four scenarios: (i) baseline, (ii) increased natural light exposure, (iii) elevated relative humidity, and (iv) increased air temperature. EEG-derived metrics of attention and relaxation were analyzed alongside environmental data, including temperature, humidity, lighting conditions, carbon dioxide (CO₂) concentration, total volatile organic compounds (TVOC), and particulate matter (PM), to identify potential correlations. Results showed that natural light exposure improved relaxation but reduced attention, suggesting a restorative effect on stress that may also introduce distractions. Attention peaked under moderately warm, dry conditions (25–26 °C and 16–19% relative humidity), correlating positively with temperature (Pearson correlation coefficient, $r$ = 0.32) and negatively with humidity ($r$ = −0.50). Conversely, relaxation was highest under cooler, more humid conditions (23–24 °C and 24–26% relative humidity). Attention was negatively correlated with CO₂ ($r$ = −0.47) and PM_2.5 ($r$ = −0.46), suggesting that poor air quality impairs alertness. Relaxation showed weaker but positive correlations with PM_2.5 ($r$ = 0.38), PM_1.0 ($r$ = 0.35), and CO₂ ($r$ = 0.32). Ultrafine particles (PM_0.3, PM_0.5) and TVOC had minimal association with cognitive states. Overall, this study underscores the importance of optimizing indoor environments in educational settings to enhance academic performance and supports the development of evidence-based design standards to foster healthy, effective learning environments. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

Neurophysiological techniques, particularly somatosensory evoked potentials (SEPs) and electroencephalography (EEG), are essential tools for the functional and prognostic evaluation of patients with prolonged disorders of consciousness (DoC) in intensive neurorehabilitation settings. This narrative review critically analyzes the most relevant evidence regarding the use of SEPs and EEG in the management of post-comatose patients, highlighting the strategic role of physiatrists in integrating these assessments into individualized rehabilitation plans. A systematic search was conducted across major international databases (PubMed, Embase, Scopus, Cinahl, and DiTA) until December 2024, selecting consensus documents, official guidelines (including the 2021 ERC/ESICM guidelines), systematic reviews, observational studies, and significant Italian neurophysiological contributions. The literature supports the strong prognostic value of the bilateral presence of the N20 component in SEPs, while its early bilateral absence, particularly in post-anoxic cases, is a robust predictor of poor neurological outcomes. EEG provides complementary information, with continuous, reactive, and symmetrical patterns associated with favorable outcomes, while pathological patterns, such as burst suppression or isoelectric activity, predict a worse prognosis. Combining SEP and EEG assessments significantly improves prognostic sensitivity and specificity, especially in sedated or metabolically compromised patients. Additionally, the use of direct muscle stimulation (DMS) and nerve conduction studies enables accurate differentiation between central and peripheral impairments, which is crucial for effective rehabilitation planning. Overall, SEPs and EEG should be systematically incorporated into the evaluation and follow-up of DoC patients, and the acquisition of neurophysiological competencies by physiatrists represents a strategic priority for modern, effective, and personalized neurorehabilitation. Full article

The acquisition of electroencephalography (EEG) concurrently with functional magnetic resonance imaging (fMRI) requires a careful consideration of the health hazards resulting from interactions between the scanner’s electromagnetic fields and EEG recording equipment. The primary safety concern is excessive RF-induced heating of the tissue in the vicinity of electrodes. We have previously demonstrated that concurrent intracranial EEG (icEEG) and fMRI data acquisitions (icEEG-fMRI) can be performed with acceptable risk in specific conditions using a head RF transmit coil. Here, we estimate the potential additional heating associated with the addition of scalp EEG electrodes using a body transmit RF coil. In this study, electrodes were placed in clinically realistic positions on a phantom in two configurations: (1) icEEG electrodes only, and (2) following the addition of subdermal scalp electrodes. Heating was measured during MRI scans using a body transmit coil with a high specific absorption rate (SAR), TSE (turbo spin echo), and low SAR gradient-echo EPI (echo-planar imaging) sequences. During the application of the high-SAR sequence, the maximum temperature change for the intracranial electrodes was +2.8 °C. The addition of the subdural scalp EEG electrodes resulted in a maximum temperature change for the intracranial electrodes of 2.1 °C and +0.6 °C across the scalp electrodes. For the low-SAR sequence, the maximum temperature increase across all intracranial and scalp electrodes was +0.7 °C; in this condition, the temperature increases around the intracranial electrodes were below the detection level. Therefore, in the experimental conditions (MRI scanner, electrode, and wire configurations) used at our centre for icEEG-fMRI, adding six scalp EEG electrodes did not result in significant additional localised RF-induced heating compared to the model using icEEG electrodes only. Full article

(1) Background: Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system. Full article

Commercial progress concerning biobased materials has been slow, with success depending on functionality and emotional responses. Emotional interaction research provides a novel way to shift perceptions of biobased materials. This study proposes a human-centered emotional design framework using biobased tea waste to explore how sensory properties (form, color, odor, surface roughness) shape emotional responses and contribute to sustainable wellbeing. We used a mixed-methods approach combining subjective evaluations (Self-Assessment Manikin scale) with physiological metrics (EEG, skin temperature, pupil dilation) from 24 participants. Results demonstrated that spherical forms and high surface roughness significantly enhanced emotional valence and arousal, while warm-toned yellow samples elicited 23% higher pleasure ratings than dark ones. Neurophysiological data revealed that positive emotions correlated with reduced alpha power in the parietal lobe (αPz, p = 0.03) and a 0.3 °C rise in skin temperature, whereas negative evaluations activated gamma oscillations in central brain regions (γCz, p = 0.02). Mapping these findings to human factors engineering principles, we developed actionable design strategies—such as texture-optimized surfaces and color–emotion pairings—that transform tea waste into emotionally resonant, sustainable products. This work advances emotional design’s role in fostering ecological sustainability and human wellbeing, demonstrating how human-centered engineering can align material functionality with psychological fulfillment. Full article

As urban traffic complexity increases, realistic multimodal simulation environments are essential for evaluating transportation safety and human behavior. This study introduces a novel multimodal, multi-participant co-simulation framework designed to comprehensively model interactions between drivers, bicyclists, and pedestrians. The framework integrates CARLA, a high-fidelity driving simulator, with PTV Vissim, a widely used microscopic traffic simulation tool. This integration was achieved through the development of custom scripts in Python and C++ that enable real-time data exchange and synchronization between the platforms. Additionally, physiological sensors, including heart rate monitors, electrodermal activity sensors, and EEG devices, were integrated using Lab Streaming Layer to capture physiological responses under different traffic conditions. Three experimental case studies validate the system’s capabilities. In the first, cyclists showed a significant rightward lane shift (from 0.94 m to 1.14 m, $p<0.00001$) and elevated heart rates (69.45 to 72.75 bpm, $p<0.00001$) in response to overtaking vehicles. In the second, pedestrians exhibited more conservative gap acceptance behavior at 50 mph vs. 30 mph (gap acceptance time: 3.70 vs. 3.18 s, $p<0.00001$), with corresponding increases in HR (3.54 bpm vs. 1.91 bpm post-event). In the third case study, mean vehicle speeds recorded during simulated driving were compared with real-world field data along urban corridors, demonstrating strong alignment and validating the system’s ability to reproduce realistic traffic conditions. These findings demonstrate the system’s effectiveness in capturing dynamic, real-time human responses and provide a foundation for advancing human-centered, multimodal traffic research. Full article

Background/Objectives: Up to 75% of patients with major depressive disorder (MDD) exhibit persistent anhedonia symptoms related to abnormalities in the positive valence system. Cumulative evidence points to brain dysfunction in the reward system (RS), including in the ventral striatum, in patients with MDD with anhedonia. This study aims to evaluate the safety and efficacy of a novel neurofeedback (NF) device (termed Prism) which incorporates the EEG–FRI-Pattern biomarker of the reward system (RS-EFP) for use in self-neuromodulation training (RS-EFP-NF) for alleviating depression in patients with MDD with anhedonia. Methods: A total of 49 adults (age range: M = 39.9 ± 11.03) with a DSM-5 diagnosis of MDD with anhedonia (per a SHAPS-C score ≥ 25) were screened for the administration of ten sessions of RS-EFP-NF twice a week on nonconsecutive days. Depression and anhedonia severity was assessed, respectively, by HDRS-17 and SHAPS-C at baseline, midway, and treatment end. Results: A total of 34 patients (77%) completed the protocol and were included in the analyses. No device-related adverse events were serious or required treatment. Depression symptoms were reduced at end of treatment as indicated by the HDRS-17, with a reduction of eight points on average (95% CI: −10.5 to −5.41, p < 0.0001), a clinical improvement rate of 78.47%, and a remission rate of 32.25%. Anhedonia, as indicated by the SHAPS-C score, was diminished, showing an average reduction of 6.3 points (95% CI: −8.51 to −4.14, p < 0.0001). Conclusions: Self-neuromodulation using RS-EFP-NF is a promising and safe treatment for MDD with anhedonia. The intervention demonstrates substantial clinical effects on both depression and anhedonia symptoms, with high patient acceptability and retention. Prism may address a critical mechanism-driven treatment gap for anhedonia that often persists despite conventional therapies. Larger controlled implementation, efficacy, and dosing studies are warranted. Full article