This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such as happiness, sadness, fear, and anger. Brain activity was recorded using the Emotiv EPOC X and NIRSport2 devices separately. The recorded signals were then processed and analyzed to identify the primary brain regions activated during the trials. Next, machine learning methods were employed to classify the evoked emotions with highest accuracy values of 71.3% for EEG data with a Multi-Layer Perceptron (MLP) method and 64.0% for fNIRS data using a Bagging Trees (BAG) algorithm. This approach not only highlights the effectiveness of using EEG and fNIRS technologies but also provides insights into the complex interplay between different brain areas during emotional experiences. By leveraging these advanced acquisition techniques, this study aims to contribute to the broader field of affective neuroscience and improve the accuracy of emotion recognition systems. The findings could have significant implications for developing intelligent systems capable of more empathetic interactions with humans, enhancing applications in areas such as mental health, human–computer interactions, or adaptive learning environments, among others.

There is growing evidence to suggest that problematic social media use reflects a variety of neurocognitive profiles. This study examined differences in absolute alpha and beta spectral power using electroencephalography across three usage profiles: Compulsive Use Profile (CUP), Distress-Associated Profile (DAP) and Functional Profile (FP), derived from a prior cluster analysis. Activity was recorded during three experimental phases: baseline rest, an adapted Go/NoGo task with social media stimuli, and recovery. A 32-channel EEG was recorded from 42 young adults ($\overline{\mathrm{x}}$ = 19.62 years) during the 3 phases mentioned. Absolute spectral power (Welch method) was calculated for the alpha and beta bands in frontal and parietal regions. Mixed ANOVAs, Mann–Whitney U pairwise comparisons and Friedman tests were performed to assess neural modulation. A cluster effect approaching, but not reaching, conventional significance was found for left frontal alpha power (p = 0.057); a direct test of the Profile × Phase interaction for this variable was not significant (p = 0.837), indicating this trend was not specific to any one experimental phase. Pairwise comparisons revealed that the CUP showed significantly lower left frontal alpha power than the FP during the task phase (p = 0.012), with no differences at rest or during recovery. The DAP group showed lower left parietal alpha power during the task (p = 0.036) and lower right parietal alpha power at rest (p = 0.034) compared with the FP group. Only the FP group exhibited significant modulation of alpha power (neural flexibility) across phases (p = 0.035). Problematic social media use is not a uniform phenomenon. The CUP and DAP show distinct neural patterns in the frontal cortex for compulsivity and in the parietal cortex for emotional vulnerability, supporting the use of differentiated neurocognitive models and the need for tailored clinical interventions.

To investigate whether electroencephalography (EEG) recorded during a brief Psychomotor Vigilance Task provides physiological information that can differentiate children previously identified as gifted from control participants in school settings. EEG was recorded in 158 schoolchildren aged 9–13 years (48 gifted, 110 controls) using a 32-channel semi-dry system (256 Hz). Each session comprised a 2-minute eyes-closed baseline followed by a 7-minute child-adapted PVT. Temporal, spectral, entropy, geometry, asymmetry, and functional connectivity features were extracted across canonical frequency bands and normalized relative to each participant’s resting baseline. Group differences were examined using non-parametric statistics, and supervised classification was evaluated using repeated cross-validation. Behavioural performance did not differ significantly between groups. EEG analyses revealed differences across several frequency bands, feature domains, and cortical regions. The highest classification performance was obtained in the delta band using logistic regression (macro-F1 = \(0.745 \pm 0.066\), AUC-ROC = \(0.815 \pm 0.049\)), while alpha and beta bands also provided discriminative information. EEG recorded during a brief cognitive task contains physiological information associated with the distinction between gifted and control participants. Combined with machine learning, this approach provides an objective and complementary source of information that may support the development of more standardized neurophysiological methods for giftedness identification.

Background: Mild Alzheimer's disease (AD) is associated with alterations in brain activity, which can be detected using electroencephalography (EEG). Investigating these changes during emotional processing may help identify neurophysiological patterns that differentiate patients with mild AD from healthy controls (HCs). Objective: To investigate whether EEG responses elicited during emotional processing provide biomarkers capable of discriminating patients with AD from HCs. Additionally, to identify the emotional contexts, brain regions, frequency bands, and machine learning models that maximize this discriminative capacity. Methods: A sample of 39 AD patients and 54 HCs watched brief movie clips designed to elicit tenderness, amusement, anger, fear, and sadness, along with an Alzheimer's-related clip, together with neutral clips used as control, baseline, and recovery conditions, while cortical activity was recorded using EEG. The signals were then classified across emotional contexts using LASSO, Random Forest, SVM-RBF, XGBoost, and CatBoost ML models. Results: EEG signals allowed for discrimination between AD patients and HCs across different emotional contexts, including individual emotions, grouped by valence or arousal, and the Alzheimer's-related stimulus. LASSO achieved the best performance for positive and neutral conditions in the parietal and posterior gamma bands, whereas CatBoost performed best for high-arousal negative emotions such as anger and fear, particularly in frontal gamma and theta bands, respectively. Conclusions: Patients with mild AD show EEG signal patterns that differ from those of HCs across different emotional contexts. These findings highlight the potential of EEG recorded during emotional processing to support the development of objective biomarkers for mild AD.

This review systematically examines electroencephalography (EEG) studies on gifted children, focusing on the signal processing pipelines across acquisition, preprocessing, feature extraction, and analysis, and identifying opportunities for methodological standardisation relevant to educational research. Following PRISMA 2020 guidelines, a comprehensive search was carried out in PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO. From 197 records, 14 studies met the inclusion criteria and were analysed for EEG setup, preprocessing strategies, and analytical approaches, including event-related potentials, spectral and connectivity measures, and applications of machine learning. Substantial heterogeneity was observed in device configurations, preprocessing practices, and analytical choices, limiting cross-study comparability and the transfer of findings to educational contexts. Nevertheless, recurring neurophysiological markers were identified, such as P300, frontoparietal $\gamma$ synchronisation, and $\theta$–$\alpha$ modulations during cognitive tasks. Only a minority of studies implemented supervised classification methods, suggesting an underexplored potential for advanced data-driven approaches in paediatric EEG. Transparent and standardised EEG pipelines, with explicit reporting of filters, artefact thresholds, and rejection rates, are essential to enhance reproducibility and translational value. By framing EEG signal processing within an educational perspective, this review provides methodological guidance to support early identification, inform classroom practice, and strengthen the bridge between neuroscience and education.