arXiv subjects

Takfarinas Medani

Publications and source records attributed to Takfarinas Medani.

16 recordsLinked to original sources

arXiv2026

Feasibility of simultaneous EEG-fMRI at 0.55 T: Recording, Denoising, and Functional Mapping

Simultaneous recording of electroencephalography (EEG) and functional MRI (fMRI) can provide a more complete view of brain function by merging high temporal and spatial resolutions. This proof-of-concept study presents initial evidence for the feasibility of simultaneous EEG-fMRI at 0.55T in a visual task. We characterize the gradient and ballistocardiogram (BCG) artifacts inherent to this environment and demonstrate that the lower field strength suggests a reduction in the magnitude of the BCG artifact compared to high-field (1.5T, 3T, 7T) systems. This reduction shows promise for facilitating effective denoising while preserving signal integrity. Furthermore, we tested a multimodal integration pipeline that uses the EEG power envelope to compute a predictor of the hemodynamic BOLD response, demonstrating the potential for EEG-based estimation of neurovascular coupling in this environment. We demonstrate that combined EEG-fMRI at 0.55T is feasible and represents a promising environment for multimodal neuroimaging.

eess.SP↗

arXiv2026

Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications

Electroencephalography (EEG) and magnetoencephalography (MEG) provide noninvasive measurements of brain activity with millisecond temporal resolution, enabling the investigation of functional and effective interactions within large-scale brain networks. This chapter presents a comprehensive overview of the methodological foundations and practical workflows for EEG/MEG-based brain network analysis. We first review the physical principles underlying EEG and MEG, emphasizing their complementary strengths and limitations. We then describe the forward and inverse problems, including subject-specific head modeling, source reconstruction techniques, and the importance of accurate anatomical modeling for reliable source localization. Strategies for mitigating volume conduction and signal leakage are discussed, together with best practices for source-space connectivity analysis. The chapter reviews widely used functional and effective connectivity measures, including coherence, phase synchronization metrics, amplitude envelope correlation, Granger causality, dynamic causal modeling, and transfer entropy, highlighting their assumptions, advantages, and limitations. Modern end-to-end analysis pipelines are presented, with particular emphasis on Brainstorm and complementary open-source software for reproducible EEG/MEG research. Finally, we discuss emerging approaches, including time-varying connectivity, cross-frequency interactions, and network-based analyses, illustrating how noninvasive electrophysiology contributes to understanding brain organization in health and disease. The chapter provides both conceptual foundations and practical guidance for researchers and advanced students seeking to map and interpret human brain networks using EEG and MEG.

q-bio.NC↗

arXiv2026

An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production

Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production chain: brain signals, muscle activations, and articulatory movements. This multimodal acquisition paradigm presents substantial technical challenges, including MRI-induced electromagnetic interference and myogenic artifacts. To mitigate these, we introduce an artifact suppression pipeline tailored to this tri-modal setting. Once fully developed, this framework is poised to offer an unprecedented window into speech neuroscience and insights leading to brain-computer interface advances. The source code and data are available.

eess.AS↗

arXiv2026

Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models

EEG foundation models, pre-trained on large-scale unlabelled EEG data, have emerged as a promising direction towards learning generalizable EEG representations. Despite showing positive results in data-rich regimes, they often fail to outperform significantly smaller supervised models in low-resource settings compared to fully supervised models. We provide a mechanistic account of this shortcoming, attributing it to a fundamental mismatch between reconstruction-based pretext tasks and the idiosyncratic spectral structure of EEG signals, which decompose into distinct high-power aperiodic and low-power oscillatory components. Using controlled, synthetically-generated EEG inputs, we demonstrate that EEG foundation model embeddings are biased to capture the aperiodic components of the EEG signal while under-representing oscillatory components, particularly at higher frequencies. Additionally, linear probe evaluations on real-world BCI datasets further reveal that embeddings encode subject identity more strongly than task-relevant information, thereby reinforcing the low-frequency and aperiodic component bias in foundation model embeddings trained primarily on reconstruction based objectives. Together, these findings elucidate a failure mode in reconstruction based EEG foundation models and motivate future work to incorporate auxiliary losses explicitly targeting high-frequency oscillatory structure as a path toward more capable and generalizable EEG representations.

cs.LG↗

arXiv2026

Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection

Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subjects due to background bias and reliance on subject-specific appearance cues. We propose a joint-centric attention model that focuses exclusively on body dynamics to improve cross-subject generalization. For each video segment, body joints are detected and joint-centered clips are extracted, suppressing background context. These joint-centered clips are tokenized using a Video Vision Transformer (ViViT), and cross-joint attention is learned to model spatial and temporal interactions between body parts, capturing coordinated movement patterns characteristic of seizure semiology. Extensive cross-subject experiments show that the proposed method consistently outperforms state-of-the-art CNN-, graph-, and transformer-based approaches on unseen subjects.

cs.CV↗

arXiv2026

Multi-Compartment Volume Conductor with Complete Electrode Model: Simulated Stereo-EEG Source Localization using Brainstorm-Zeffiro Plugin

This study introduces a novel integration of the Brainstorm (BST) software and the Zeffiro Interface (ZI) to enable whole-head, multi-compartment volume conductor modeling for electroencephalography (EEG) source imaging, with a particular focus on stereotactic EEG applications. We present the BST-2-ZI plugin, a MATLAB-based tool that facilitates seamless transfer of tissue segmentations and anatomical atlases from BST into ZI for finite element (FE) mesh generation as well as forward and inverse modeling. The generated FE meshes support variable spatial resolution and implement the complete electrode model (CEM), allowing for precise modeling of both invasive depth electrodes and non-invasive scalp electrodes. Using the ICBM152 template and synthetic source simulation, we demonstrate the end-to-end pipeline from MRI data to lead field (LF) computation and source localization in a stereotactic EEG (stereo-EEG) setting. Our numerical experiments highlight the capability of the pipeline to accurately model multi-compartment head geometry and conductivity with a stereotactic CEM-based electrode configuration. Our preliminary source localization results show how a synthetic stereo-EEG probe corresponding to a bidirectional deep brain stimulation (DBS) probe with four omnidirectional contacts can, in principle, be coupled with scalp electrodes to improve source localization in its vicinity.

physics.med-ph↗

arXiv2025

Informed Bootstrap Augmentation Improves EEG Decoding

Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness and can degrade representational quality. We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples. In a Sentence Evaluation paradigm, weights were computed from relative ERP differences and applied during probabilistic sampling and averaging. Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best), demonstrating that emphasizing reliable trials strengthens representational quality. The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations. The code is publicly available at https://github.com/lyricists/NeuroBootstrap.

eess.SP↗

arXiv2025

Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality

Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal dynamics of affective semantic processing using multivariate decoding of time-resolved, 64-channel electroencephalography (EEG). Participants (N=137) performed a sentence-evaluation task with emotionally salient, self-referential statements. We identified robust neural signatures of semantic processing, with peak decoding accuracy between 300-600 ms -- a window associated with rapid, stimulus-driven semantic evaluation and conflict monitoring. Relative to healthy controls, individuals with depression and suicidal ideation showed earlier onset, longer duration, and greater amplitude decoding responses, along with broader cross-temporal generalization and enhanced contributions from frontocentral and parietotemporal components. These findings suggest altered sensitivity and impaired disengagement from emotionally salient content in the clinical groups, advancing our understanding of the neurocognitive basis of mental health and establishing a compact and interpretable EEG-based index of semantic-evaluation dynamics with potential diagnostic relevance.

q-bio.NC↗

arXiv2025

A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation

Accurate identification of mental health biomarkers can enable earlier detection and objective assessment of compromised mental well-being. In this study, we analyze electrodermal activity recorded during an Emotional Stroop task to capture sympathetic arousal dynamics associated with depression and suicidal ideation. We model the timing of skin conductance responses as a point process whose conditional intensity is modulated by task-based covariates, including stimulus valence, reaction time, and response accuracy. The resulting subject-specific parameter vector serves as input to a machine learning classifier for distinguishing individuals with and without depression. Our results show that the model parameters encode meaningful physiological differences associated with depressive symptomatology and yield superior classification performance compared to conventional feature extraction methods.

eess.SP↗

arXiv2025

High-Resolution Directional Depth Electrodes: Open-Source FEM Lead-Field Modeling, Characterization, and Validation

Depth electrodes used in stereoelectroencephalography (sEEG) and deep-brain stimulation (DBS) are essential tools for neural recording and stimulation. Traditional designs have limited spatial resolution, typically 8 to 16 cylindrical contacts (0.8 to 1.0 mm diameter) along a 5 to 10 cm shaft, restricting recordings from small or localized populations. Recent high-density, directional electrodes (HDsEEG) enable finer localization of local field potentials (LFPs) and spike timing. Yet, characterizing their directional sensitivity and validating modeling tools for lead field (LF) analysis remain critical. We compare finite element method (FEM) LF modeling of a novel HD-sEEG electrode using two tools: a commercial solver (ANSYS) and an open-source pipeline (Brainstorm-DUNEuro). Goals: (i) validate against analytical solutions, (ii) assess solver differences, and (iii) characterize HDsEEG directional sensitivity. LFs were modeled in simple and bio-relevant scenarios. Using Helmholtz reciprocity, we computed LFs by (a) electrode-based stimulation with ANSYS and (b) source-based recording with Brainstorm-DUNEuro. First, a multi-sphere head model with known solutions tested the solver's accuracy. Next, an HDsEEG electrode in a homogeneous conductor was simulated. Directional effects were assessed by comparing sensitivity with vs. without the insulating substrate. Source localization performance was also compared between HDsEEG and standard electrodes. Both solvers closely matched analytic solutions. In realistic settings, LF distributions were highly similar. Modeling showed clear directional sensitivity: contacts facing a source had higher sensitivity than those shadowed, reflecting a substrate shielding effect that vanished when the substrate was conductive. Crucially, HDsEEG improved source localization, as voltage differences across contacts provided robust directional LF information.

physics.med-ph↗

arXiv2025

Neural Responses to Affective Sentences Reveal Signatures of Depression

Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected. We investigate how depression alters the temporal dynamics of emotional processing by measuring neural responses to self-referential affective sentences using surface electroencephalography (EEG) in healthy and depressed individuals. Our results reveal significant group-level differences in neural activity during sentence viewing, suggesting disrupted integration of emotional and self-referential information in depression. Deep learning model trained on these responses achieves an area under the receiver operating curve (AUC) of 0.707 in distinguishing healthy from depressed participants, and 0.624 in differentiating depressed subgroups with and without suicidal ideation. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression that may inform future diagnostic tools.

cs.LG↗

arXiv2025

A Feasibility Study of Task-Based fMRI at 0.55 T

0.55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings. However, reliable task-based fMRI at 0.55T has not been significantly demonstrated. In this study, we establish a robust task-based fMRI protocol and analysis pipeline at 0.55T that achieves full brain coverage and results comparable to what is expected for activation extent and location. We attempted fMRI at 0.55T by combining EPI acquisition with custom analysis techniques. Finger-tapping and visual tasks were used, comparing 5- and 10-minute runs to enhance activation detection. The results show significant activations, demonstrating that high-quality task-based fMRI is achievable at 0.55T in single subjects. This study demonstrates that reliable task-based fMRI is feasible on 0.55T scanners, potentially broadening functional neuroimaging access in clinical and research settings where high-field MRI is unavailable or impractical, supporting broader diagnostic and research applications.

eess.IV↗

arXiv2025

Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements

Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objective biomarkers, with screening and diagnosis primarily relying on self-reports and clinical interviews. Here, we investigate eye tracking as a potential marker modality for screening purposes. Eye movements are directly modulated by neuronal networks and have been associated with attentional and mood-related patterns; however, their predictive value for depression and suicidality remains unclear. We recorded eye-tracking sequences from 126 young adults as they read and responded to affective sentences, and subsequently developed a deep learning framework to predict their clinical status. The proposed model included separate branches for trials of positive and negative sentiment, and used 2D time-series representations to account for both intra-trial and inter-trial variations. We were able to identify depression and suicidal ideation with an area under the receiver operating curve (AUC) of 0.793 (95% CI: 0.765-0.819) against healthy controls, and suicidality specifically with 0.826 AUC (95% CI: 0.797-0.852). The model also exhibited moderate, yet significant, accuracy in differentiating depressed from suicidal participants, with 0.609 AUC (95% CI 0.571-0.646). Discriminative patterns emerge more strongly when assessing the data relative to response generation than relative to the onset time of the final word of the sentences. The most pronounced effects were observed for negative-sentiment sentences, that are congruent to depressed and suicidal participants. Our findings highlight eye tracking as an objective tool for mental health assessment and underscore the modulatory impact of emotional stimuli on cognitive processes affecting oculomotor control.

cs.LG↗

arXiv2024

Neuro-GPT: Towards A Foundation Model for EEG

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

cs.LG↗

arXiv2023

The dual-path hypothesis for the emergence of anosognosia in Alzheimer's disease

Although neurocognitive models have been proposed to explain anosognosia in Alzheimer's disease (AD), the neural cascade responsible for its origin in the human brain remains unknown. Here, we build on a mechanistic dual-path hypothesis that brings error-monitoring and emotional processing systems as key elements for self-awareness, with distinct impacts on the emergence of anosognosia in AD. Proceeding from the notion of anosognosia as a dimensional syndrome, ranging from the lack of concern about one's own deficits (i.e., anosodiaphoria) to the complete lack of awareness of deficits, our hypothesis states that (i) unawareness of deficits would result from a failure in the error-monitoring system, whereas (ii) anosodiaphoria would more likely result from an imbalance between emotional processing and error-monitoring systems. In the first case, a synaptic failure in the error-monitoring system, in which the cingulate cortex plays a major role, would have a negative impact on error (or deficits) awareness, preventing patients from becoming aware of their condition. In the second case, an impairment in the emotional processing system, in which the amygdala and orbitofrontal cortex play a major role, would prevent patients from monitoring the internal milieu for relevant errors (or deficits) and assigning appropriate value to them, thus biasing their impact on the error-monitoring system. Our hypothesis stems from two scientific premises. One comes from preliminary results in AD patients showing a synaptic failure in the error-monitoring system and decline of awareness at the time of diagnosis. Another comes from the somatic marker hypothesis, which proposes that emotional signals are critical to adaptive behavior. Further exploration will be of great interest to illuminate the foundations of self-awareness and improve our understanding of the underlying mechanisms of anosognosia in AD.

q-bio.NC↗

arXiv2020

Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution

Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable of being recorded, often simultaneously, for use in research and clinical applications. The so-called forward problem is the modeling of these fields at their sensors for a given putative neural source configuration. While early generations modeled the head as a simple set of isotropic spheres, today s ubiquitous magnetic resonance imaging (MRI) data allows detailed descriptions of head compartments with assigned isotropic and anisotropic conductivities. In this paper, we present a complete pipeline, integrated into the Brainstorm software, that allows users to generate an individual and accurate head model from the MRI and then calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by the integration of the latest tools for MRI segmentation and FEM mesh generation. The final head model is divided into five main compartments: white matter, grey matter, CSF, skull, and scalp. For the isotropic compartments, widely-used default conductivity values are assigned. For the brain tissues, we use the process of the effective medium approach (EMA) to estimate anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic calculations are performed by the DUNEuro library, integrated into Brainstorm and accessible with a user-friendly graphical interface. This integrated pipeline, with full tutorials and example data sets freely available on the Brainstorm website, gives the neuroscience community easy access to advanced tools for electromagnetic modeling using FEM.

physics.med-ph↗