Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis.

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

A low-cost and open-hardware portable 3-electrode sleep monitoring device.

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

Directed brain interactions over time: A resting-state EEG comparison between schizophrenia and healthy individuals.

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns.

Sleep Consolidation Potentiates Sensorimotor Adaptation.

Contrary to its well-established role in declarative learning, the impact of sleep on motor memory consolidation remains a subject of debate. Current literature suggests that while motor skill learning benefits from sleep, consolidation of sensorimotor adaptation (SMA) depends solely on the passage of time. This has led to the proposal that SMA may be an exception to other types of memories. Here, we addressed this ongoing controversy in humans through three comprehensive experiments using the visuomotor adaptation paradigm (N = 290, 150 females). In Experiment 1, we investigated the impact of sleep on memory retention when the temporal gap between training and sleep was not controlled. In line with the previous literature, we found that memory consolidates with the passage of time. In Experiment 2, we used an anterograde interference protocol to determine the time window during which SMA memory is most fragile and, thus, potentially most sensitive to sleep intervention. Our results show that memory is most vulnerable during the initial hour post-training. Building on this insight, in Experiment 3, we investigated the impact of sleep when it coincided with the critical first hour of memory consolidation. This manipulation unveiled a benefit of sleep (30% memory enhancement) alongside an increase in spindle density and spindle-SO coupling during NREM sleep, two well-established neural markers of sleep consolidation. Our findings reconcile seemingly conflicting perspectives on the active role of sleep in motor learning and point to common mechanisms at the basis of memory formation.

Electroencephalographic and Behavioral Effects of Intranasal Administration of a Na+, K+-ATPase-Activating Antibody after Status Epilepticus.

Status epilepticus (SE) is a medical emergency associated with high mortality and morbidity. Na+, K+-ATPase, is a promising therapeutic target for SE, given its critical role in regulation of neuron excitability and cellular homeostasis. We investigated the effects of a Na+, K+-ATPase-activating antibody (DRRSAb) on short-term electrophysiological and behavioral consequences of pilocarpine-induced SE. Rats were submitted to pilocarpine-induced SE, followed by intranasal administration (2 µg/nostril). The antibody increased EEG activity following SE, namely, EEG power in theta, beta, and gamma frequency bands, assessed by quantitative analysis of EEG power spectra. One week later, DRRSAb-treated animals displayed less behavioral hyperreactivity in pick-up tests and better performance in novel object recognition tests, indicating that the intranasal administration of this Na+, K+-ATPase activator immediately after SE improves behavioral outcomes at a later time point. These results suggest that Na+, K+-ATPase activation warrants further investigation as an adjunctive therapeutic strategy for SE.

Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders.

OBJECTIVE: To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD). STUDY DESIGN: A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression. RESULTS: Among 151 patients, 17.2% (n = 26) developed unprovoked seizures (Sz group), while 82.8% (n = 125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs 20.0%, P = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, P = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .046 significantly increased the risk of developing unprovoked seizures. CONCLUSION: Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.

Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking.

It has been indicated that extreme sport activities result in a highly rewarding experience, despite also providing fear, stress and anxiety. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate-HR, and breathing rate-BR) and neural (electroencephalographic-EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m-low-walk- and 45 m-high-walk-from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network-VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network-DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a 'task-constantly-opened-to-subtle-information experience', rather than a 'task-focused experience'.

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

Effective brain connectivity related to non-painful thermal stimuli using EEG.

Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen-Shannon Distance.

Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen-Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks.

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Brain wave oscillations as an objective neurophysiological biomarker of homeopathic subjective well-being [Pré-print]

Homeopathy uses the "similitude principle" to arouse a therapeutic reaction in the body against its own disorders. For this to occur optimally, the medicinal pathogenetic effects must present similarity with the totality of the individual's symptoms. To assess if this similarity has been successfully achieved, Hahnemann states that "improvement in the disposition and mind"­i.e., subjective well-being­is the most important parameter to consider. Aim Our aim was to perform a narrative review of the literature, exploring what is known about subjective well-being as a marker of therapeutic action, and to formulate ways in which subjective well-being might be quantifiable and applied in future homeopathy research. The concept of subjective well-being has been extensively studied in the complementary and conventional medical literature. Improved well-being has been observed in clinical trials, including those in the fields of positive psychology and meditation. Positive subjective outcomes of this nature are supported by objective evidence through associated changes in brain oscillatory activity using electroencephalography and/or "brain mapping" by functional magnetic resonance imaging. Neurophysiological responses in the brain have been identified in subjects after they ingested a homeopathic medicine. The concept of subjective well-being is supported by a body of literature and is a measurable entity. When viewed from the perspective of electrophysiological changes, brain activity is an objective neurophysiological biomarker with a potential to quantify individual well-being in the context of homeopathy research.

Evaluation of the Relation between Ictal EEG Features and XAI Explanations.

Epilepsy is a neurological disease with one of the highest rates of incidence worldwide. Although EEG is a crucial tool for its diagnosis, the manual detection of epileptic seizures is time consuming. Automated methods are needed to streamline this process; although there are already several works that have achieved this, the process by which it is executed remains a black box that prevents understanding of the ways in which machine learning algorithms make their decisions. A state-of-the-art deep learning model for seizure detection and three EEG databases were chosen for this study. The developed models were trained and evaluated under different conditions (i.e., three distinct levels of overlap among the chosen EEG data windows). The classifiers with the best performance were selected, then Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) were employed to estimate the importance value of each EEG channel and the Spearman's rank correlation coefficient was computed between the EEG features of epileptic signals and the importance values. The results show that the database and training conditions may affect a classifier's performance. The most significant accuracy rates were 0.84, 0.73, and 0.64 for the CHB-MIT, Siena, and TUSZ EEG datasets, respectively. In addition, most EEG features displayed negligible or low correlation with the importance values. Finally, it was concluded that a correlation between the EEG features and the importance values (generated by SHAP and LIME) may have been absent even for the high-performance models.

Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Exposure to high-altitude hypoxia, with reduced oxygen levels, can replicate brain function changes akin to aging and Alzheimer's disease. In our work, we propose high-altitude hypoxia as a possible reversible model of human brain aging. We gathered EEG data at high altitude and sea level, investigating the impact of hypoxia on brainwave patterns and connectivity. Our findings revealed that high-altitude exposure led to slower and noisier brain oscillations and produced altered brain connectivity, resembling some remarkable changes seen in the aging process. Intriguingly, these changes were linked to age, even when hypoxia's effects were considered. Our research unveils how high-altitude conditions emulate brain patterns associated with aging and neurodegenerative conditions, providing valuable insights into the understanding of both normal and impaired brain function.

Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties.

Introduction: Assistive technologies for learning are aimed at promoting academic skills, such as reading and mathematics. These technologies mainly embrace mobile and web apps addressed to children with learning difficulties. Nevertheless, most applications lack pedagogical foundation. Additionally, the task of selecting suitable technology for educational purposes becomes challenging. Hence, this protocol posits the psychophysiological assessment of an online method for learning (OML) named Smartick. This platform comprises reading and math activities for learning training. In this protocol, individual monitoring of each child is proposed to determine the progress in learning caused by Smartick. Methods and analysis: One hundred and twelve children aged between 8 and 12 who present reading or math difficulty after a rigorous psychometric evaluation will be recruited. The study comprises four sessions. In sessions 1 and 2, collective and individual psychometric evaluations will be performed, respectively. Reading and mathematical proficiency will be assessed, as well as attentional levels and intellectual quotient. Subsequently, each child will be semi-randomly assigned to either the experimental or control groups. Afterward, a first EEG will be collected for all children in session 3. Then, experimental groups will use Smartick for 3 months, in addition to their traditional learning method. In contrast, control groups will only continue with their traditional learning method. Finally, session 4 will consist of performing a second psychometric evaluation and another EEG, so that psychophysiological parameters can be encountered that indicate learning improvements due to the OML, regardless of the traditional learning method at hand. Discussion: Currently, few studies have validated learning improvement due to assistive technologies for learning. However, this proposal presents a psychophysiological evaluation addressed to children with reading or math difficulties who will be trained with an OML.

Using EEG total energy as a noninvasively tracking of intracranial (and cerebral perfussion) pressure in an animal model: A pilot study.

PURPOSE: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH. NEW METHODS: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH. RESULTS: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7. COMPARISON WITH EXISTING METHODS: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance. CONCLUSION: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.

Stereo-EEG features of temporal and frontal lobe seizures with loss of consciousness.

The loss of consciousness (LOC) during seizures is one of the most striking features that significantly impact the quality of life, even though the neuronal network involved is not fully comprehended. We analyzed the intracerebral patterns in patients with focal drug-resistant epilepsy, both with and without LOC. We assessed the localization, lateralization, stereo electroencephalography (SEEG) patterns, seizure duration, and the quantification of contacts exhibiting electrical discharge. The degree of LOC was quantified using the Consciousness Seizure Scale. Thirteen patients (40 seizures) with focal drug-resistant epilepsy underwent SEEG. In cases of temporal lobe epilepsy (TLE, 6 patients and 15 seizures), LOC occurred more frequently in seizures with mesial rather than lateral temporal lobe onset. On the other hand, in cases of frontal lobe epilepsy (7 patients; 25 seizures), LOC was associated with pre-frontal onset, a higher number of contacts with epileptic discharge compared to the onset count and longer seizure durations. Our study revealed distinct characteristics during LOC depending on the epileptogenic zone. For temporal lobe seizures, LOC was associated with mesial seizure onset, whereas in frontal lobe epilepsy, seizure with LOC has a significant increase in contact showing epileptiform discharge and a pre-frontal onset. This phenomenon may be correlated with the broad neural network required to maintain consciousness, which can be affected in different ways, resulting in LOC.

Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.

Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms.

Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.