6 citations · 9 across the 12 of their papers we have counts for
16 papers
Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
Parsa Razmara, Woojae Jeong, Aditya Kommineni +3
Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts,…
Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications
Richard Leahy, Takfarinas Medani
Electroencephalography (EEG) and magnetoencephalography (MEG) provide noninvasive measurements of brain activity with millisecond temporal resolution, enabling the investigation of…
Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models
Aditya Kommineni, Emily Zhou, Kleanthis Avramidis +7
EEG foundation models, pre-trained on large-scale unlabelled EEG data, have emerged as a promising direction towards learning generalizable EEG representations. Despite showing pos…
Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection
Omar Zamzam, Takfarinas Medani, Chinmay Chinara +1
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…
Feasibility of simultaneous EEG-fMRI at 0.55 T: Recording, Denoising, and Functional Mapping
Parsa Razmara, Takfarinas Medani, Majid Abbasi Sisara +6
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…
Multi-Compartment Volume Conductor with Complete Electrode Model: Simulated Stereo-EEG Source Localization using Brainstorm-Zeffiro Plugin
Fernando Galaz Prieto, Takfarinas Medani, Chinmay Chinara +2
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 elec…
Informed Bootstrap Augmentation Improves EEG Decoding
Woojae Jeong, Wenhui Cui, Kleanthis Avramidis +3
Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restri…
A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation
Kleanthis Avramidis, Myzelle Hughes, Idan A Blank +5
Accurate identification of mental health biomarkers can enable earlier detection and objective assessment of compromised mental well-being. In this study, we analyze electrodermal…
High-Resolution Directional Depth Electrodes: Open-Source FEM Lead-Field Modeling, Characterization, and Validation
Takfarinas Medani, Jace Willis, Chris Wright +7
Depth electrodes used in stereoelectroencephalography (sEEG) and deep-brain stimulation (DBS) are essential tools for neural recording and stimulation. Traditional designs have lim…
Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
Woojae Jeong, Aditya Kommineni, Kleanthis Avramidis +15
Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal…
Neural Responses to Affective Sentences Reveal Signatures of Depression
Aditya Kommineni, Woojae Jeong, Kleanthis Avramidis +13
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 fu…
A Feasibility Study of Task-Based fMRI at 0.55 T
Parsa Razmara, Takfarinas Medani, Anand A. Joshi +6
0.55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings. However,…
Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements
Kleanthis Avramidis, Woojae Jeong, Aditya Kommineni +14
Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objec…
Neuro-GPT: Towards A Foundation Model for EEG
Wenhui Cui, Woojae Jeong, Philipp Thölke +4
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 se…
The dual-path hypothesis for the emergence of anosognosia in Alzheimer's disease
Katia Andrade, Thomas Guiyesse, Takfarinas Medani +3
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 unkno…
Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution
Takfarinas Medani, Juan Garcia-Prieto, Francois Tadel +6
Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable…