Emotional recognition is a fundamental task towards the true understanding of the instantaneous brain state. Promising results using electroencephalography (EEG) for emotion detection have been presented, yet most approaches are constrained to laboratory settings with limited ecological validity. To address this, we employed an immersive curved-screen projection environment, chosen for its ability to preserve ecological validity while minimizing EEG signal artifacts commonly induced by virtual reality headsets. We present a novel EEG dynamic functional connectivity (dFC) framework for emotional recognition by using the Neural Gas algorithm for capturing the rapid EEG alterations in an immersive projection environment. EEG responses from 33 participants were collected during video stimulus presentations featuring five emotional environments: calm beach, calm nature, shark attack, rollercoaster, and night walk. The EEG acquisition employed a Unicorn Hybrid Black 8-channel system, while the dFC is computed by using weighted Phase Lag Index (wPLI) through a shifting window-based approach. The symbolic time series are built through Neural Gas, and chronnectomics are then calculated. Statistically significant differences were observed in the brain state flexibility across emotional conditions, with calm conditions showing enhanced flexibility (Nature: 0.93, Beach: 0.93) compared to stress conditions (Shark Attack: 0.75, Night Walk: 0.77). The flexibility index emerged as the primary discriminator of emotional states, with all stress versus calm comparisons achieving statistical significance$(p<0.001)$. Mean dwell time and occupancy entropy provided complementary insights into network stability and state diversity. This advanced EEG DFG study in an immersive real-world environment demonstrates superior discrimination of emotional states compared to traditional laboratory settings, establishing new methodological standards for ecological emotion recognition research.

Accurate classification of lung cancer subtypes from histopathological images is challenging due to limited labelled data and high visual similarity between classes. This limitation is especially critical in clinical diagnostics, where timely and accurate classification can impact treatment decisions. To address this, LungCLR presents a comprehensive evaluation of a two-stage framework that combines contrastive self-supervised learning with EfficientNet-B3 for effective feature extraction. In the first stage, the SimCLR framework is employed to pretrain the encoder on unlabeled histopathology images, enabling it to learn meaningful representations by distinguishing subtle morphological variations. A projection head is incorporated to optimise the NT-Xent loss during training. In the second stage, a lightweight classification head is attached and fine-tuned using small labelled subsets, as few as$\mathbf{1 0 0}$samples per class. Finally, partial end-to-end fine-tuning is applied to further enhance performance. LungCLR is evaluated on the LC25000 dataset, which includes three tissue categories: benign, adenocarcinoma, and squamous cell carcinoma. Using the full dataset, the proposed model achieves an impressive accuracy of 99.97 %, outperforming previous state-of-the-art methods. Importantly, even under low data conditions, the model performs robustly, reaching 90.03% accuracy with only 100 labelled samples per class. These results highlight the effectiveness of established contrastive learning techniques when carefully applied in a clinically relevant, lowdata setting.

PulSense is a portable, low-cost and multi-sensor Cardiovascular monitoring and Arrhythmia detection system. The Movesense Medical (MD) multisensory device is used to capture single-lead electrocardiogram (ECG) signals and is integrated into a Raspberry Pi 4 to perform signal processing and arrhythmia detection for first time. The system features a new user-friendly interface. It employs machine learning by a Convolutional Neural Network (CNN) trained on the MIT-BIH Arrhythmia Database (110,000 multi-labeled heartbeats) to accurately classify arrhythmia types. Feature extraction is enhanced by applying a median filter followed by a notch filter and a Continuous Wavelet Transform (CWT). High overall F1 scores were observed in different classes compared to the literature. Although the PulSense prototype is in a continuous phase of development and testing, the experimental results are encouraging and support its further development into a viable solution for constant heart monitoring and the timely detection of cardiovascular diseases in clinical and non-clinical environments.

The present study explores the effect of multichannel transcranial Direct Current Stimulation (mc-tDCS) on directed EEG connectivity patterns in a patient with refractory focal epilepsy. A double-blind, sham-controlled N -of- 1 trial was conducted, where mc-tDCS was applied over a two-week period, with EEG recordings acquired before and after stimulation and sham procedures accordingly. After artifact reduction on the EEG recordings, Generalized Partial Directed Coherence (gPDC) was utilized, to investigate effective connectivity alterations in the patient's EEG recordings. Machine learning models were also employed to evaluate the connectivity findings and the interictal spike-related class (spike / non-spike) separability. The connectivity analysis demonstrated a significant reduction in gPDC connectivity around the key EEG channels associated with epileptic activity, specifically interictal epileptiform discharges (IEDs), following mc-tDCS, with no significant changes observed in the sham condition. Following feature extraction from the connectivity analysis, machine learning validation supported these findings, revealing a potential decrease in the severity of epileptic activity, as indicated by IEDs. The results suggest that mc-tDCS effectively moderates brain connectivity in refractory focal epilepsy, with implications for reducing the frequency of IEDs. This study highlights the potential of integrating advanced connectivity analysis with machine learning for evaluating mctDCS and similar neuromodulation therapies in epilepsy, particularly in modulating pathological brain network dynamics associated with epileptic discharges.

The detection of brain functional alterations related to Epilepsy is crucial in tackling this condition and administering effective patient care. In the last years, several methods have been proposed for accurate non-invasive temporal detection and characterization of seizures in epilepsy using electroencephalography (EEG) data as input. These methods usually follow machine or deep learning frameworks with the majority of the proposed solutions being supervised. In this study, we propose an unsupervised approach for seizure detection using deep learning-based autoencoder-derived features from EEG data. Our method employs deep learning techniques and Markov Chain modeling to automatically identify subtle alterations in high-frequency EEG temporal dynamics associated with seizures. The openly available CHB-MIT database was used for our evaluations. From the results, we demonstrate the potential of the proposed approach in detecting abnormal dynamic behaviors potentially associated with ictal events, achieving ROC-AUC scores in the range of 84-100%, without depending on extensive labeling efforts. The proposed pipeline potentially contributes towards the automation of detecting seizure onset in epilepsy.

Electroencephalography (EEG)-based classification of brain disease such as epilepsy or schizophrenia, decoding brain activity during movement and vision have been shown promising results in the last years. Here, we introduce a novel pipeline for the presence of speech information carried on EEG signals. The proposed work includes a new conducted EEG dataset of 15 subjects and a deep learning model to predict the colour information. With a unique experimental set up, the data successfully captures the information about the mental enunciation of the set of used colors. The primary goal is to perform multiclass classification using our custom EEG data which records the brain activity of individuals during mental enunciation and thought about a class of objects, in our case, colours. Continuous Wavelet Transform (CWT) is applied on each of the EEG channels of each participant to obtain time-frequency (TF) based characteristics. A Vision Transformer (ViT) based model is then developed and used to capture information from these TF. The method deals with a 6-class classification problem, for which, the 6 different colors are used as target classes for our model. The proposed model achieves 91.36% cross validation accuracy, 5.48x the random guess accuracy. These results clearly demonstrate the existence of speech information in EEG signals and lay the foundational stone for future research in speech assistive technologies.

Recent modeling of brain activities encompasses the fusion of different modalities. However, fusing brain modalities requires not only the efficient and compatible representation of the signals but also the benefits associated with it. For instance, the combination of the functional characteristics of EEGs with the structural features of functional magnetic resonance imaging contributes to a better interpretation localization of brain activities. In this paper, we consider the EEG signals as parallel 2D string images from which we extract their visual abstract representations of EEG features. This representation can benefit not only the EEG modeling of the signals but also a future fusion with another modality, like fMRI. In particular, the new methodology, called Bar-LG, provides a reduced discretization of the EEG signals into selected minima/maxima in order to be used in a form of tokens for EEG brain activities of interest. A formal context-free language is used to express and represent the extracted tokens for the selected active brain regions. Then, a Generalized Stochastic Petri-Nets (GSPN) model is used for expressing the functional associations and interactions of these EEG signals as 2D image regions. An illustrative EEG example of epileptic seizure is presented to show the Bar-LG methodology's abstract capabilities.

During sleep., breathing-related sleep disorders (BSD) are very probable to cause distortions on human health and even be life-threatening. Among the different types of BSD., apnea accounts for one of the most common. Many detection algorithms have been proposed for spotting and classifying apneas, using one feature or being designed for binary classification. Also, many proposed clinical setups for respiratory data acquisition are invasive, making the application to patients a non-trial task. In this study, we aim to propose an easy-to-apply and patient-friendly clinical setup with a BSD detection that utilizes a multi-feature classification scheme for binary (apnea, healthy), as well as multiple classes (healthy, central, mixed, and obstructive apneas and hypopneas). Our clinical setup includes a high-resolution microphone attached to the bed at a very close distance to the patient. Our multi-feature approach contains spectral, statistical, and symbolic-based characteristics of respiratory signals of five patients admitted for a first BSD diagnosis and assesses the performance of different classification algorithms iteratively. The results show a high classification performance ($>$98% for binary and$>$84% for multi-class classification) for either classification scheme. A robust classification scheme is thus proposed, utilizing the entire content of the recorded respiratory signal. Such a classification scheme leads to a promising result towards the design of portable devices with multi-features for real-time detection of BSD.

Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.

Epilepsy is one of the most common brain disorders worldwide. The basic principle in epilepsy is to resect the epileptogenic zone (EZ) when the medicaments are inadequate to suppress epileptic seizures. Epilepsy is accompanied by interictal spikes, a surrogate marker serving as an identifier of seizures. The automatic temporal detection of these spikes is of major importance due to the demanding time consumption of the manual annotation. Electro- and magneto- encephalography (EEG and MEG) are the most usual measurement modalities for the recording of brain activity. EEG and MEG are ideal modalities for the non-invasive monitoring of drug-resistant epilepsy. Many approaches have been proposed for the temporal detection of interictal spikes. However, only single measurement modality (EEG or MEG) has been used up to now, neglecting their complementary content. In this study, we develop a multi-feature and iterative classification scheme with input from either single modality (EEG or MEG) or combined EEG/MEG (EMEG). The inputs include statistical (kurtosis and Renyi Entropy) and spectral (Energy) features as well as the functional connectivity metrics, global and local efficiency from imaginary phase lag index networks. The classification performance for all modalities ranges from 89% to 92.8%, with the maximum performance being observed for EMEG. Overall, the complementarity of EEG and MEG on the detection of interictal spikes is promising, opening new considerations on the development of automatic epileptic spike detection approaches.

The principle of epilepsy surgery in patients with drug-resistant focal epilepsy is to localize and then to resect the epileptogenic zone. However, epilepsy surgery might not be feasible if a cortical malformation or focal cortical dysplasia (FCD), is located very close to eloquent areas of the brain. Non-invasive brain stimulation is a promising technique for modulating brain activity and may become a neurotherapeutic approach for suppressing long term epileptic seizures. In the present study, we optimize a multi-channel transcranial direct current stimulation (tDCS) montage based on Electro-(EEG) and Magneto-Encephalography (MEG) source analysis for the therapeutic stimulation of a patient with drug-resistant epilepsy due to an FCD located very close to Broca's area. We first construct a realistic volume conductor Finite Element Method (FEM) model of the patient's head, including skull defects, calibrated skull conductivities and white matter conductivity anisotropy. Single modality (EEG or MEG) and combined EEG/MEG (EMEG) source analysis is performed for localizing the irritative zone that caused interictal epileptic discharges (IEDs). We then adopt a novel optimization algorithm, Alternating Direction Method of Multipliers (ADMM), in order to optimize the multichannel tDCS montage for distributing the injected currents in the target brain region. The patient's source analysis indicates localizations very close to the FCD and orientations to a different cortical side depending on the used measurement modality. The resulting tDCS optimized montage is based on the source reconstruction which is closer to the FCD and the occurred stimulation montage is focal over the detected FCD. The combination of individual source analysis for targeting and optimization algorithms for the estimation of a tDCS montage is a promising neurotherapeutic approach of suppressing long term epileptic seizures.

In recent years, several approaches have been introduced for estimating the spike onset zone within the irritative zone in epilepsy diagnosis for presurgical planning. One important direction utilizes source analysis from combined electroencephalography (EEG) and magnetoencephalography (MEG), EMEG, leveraging the benefits from the complementary properties of the two modalities. For EMEG source reconstruction, an average across the annotated epileptic spikes is often used to improve the signal-to-noise-ratio (SNR). In this contribution, we propose a two-phase clustering of interictal spikes with unsupervised learning methods, namely Self Organizing Maps (SOM) and K-means. In addition, we investigate the accuracy of combined EMEG source analysis on the sorted activity, using an individualized (with regard to both geometry and conductivity) six-compartment finite element head model with calibrated skull conductivity and white matter conductivity anisotropy. The results indicate that SOM eliminates the random variations of K-means and stabilizes the clustering efficiency. In terms of source reconstruction accuracy, this study demonstrates that the combined use of modalities reveals activity around two focal cortical dysplasias (FCDs), of one epilepsy patient, one in the right frontal area and one smaller in the left premotor cortex. It is worth mentioning that only EMEG could localize the left premotor FCD, which was then also found in surgery to be the responsible for triggering the epilepsy.

The primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations.

Human brain is the most complicated network and its functional mechanism is a demanding concept in neuroscience research. Graph theory and forms an interesting tool for modeling the brain interactions and estimated brain parameters. In this paper, we consider synchronization features for modeling brain operations in electro-encephalogram (EEG) responses to kanizsa and fractal stimuli, using minimal spanning tree (MST) on a network of phase synchronization EEG channels. Graphs of phase-synchronization activity and MST structures are computed using these graphs. The proposed approach yields evidence that the fractal stimuli generate stronger energy response and synchronization of theta band in occipital lobe.

The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy over other modalities. In this paper we consider synchronization features for identifying brain operations. Independent Component Analysis (ICA) is applied on MEG surface signals in controls and children with reading difficulties and are clustered to representative components. Then, coupling measures of mutual information and partial directed coherence are estimated in order to reveal dysfunction of cerebral networks and its related coordination.