1 citations · 1 across the 9 of their papers we have counts for
12 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,…
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…
An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production
Jihwan Lee, Parsa Razmara, Kevin Huang +16
Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessi…
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…
Neural Codecs as Biosignal Tokenizers
Kleanthis Avramidis, Tiantian Feng, Woojae Jeong +4
Neurophysiological recordings such as electroencephalography (EEG) offer accessible and minimally invasive means of estimating physiological activity for applications in healthcare…
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…
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…
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…
Knowledge-guided EEG Representation Learning
Aditya Kommineni, Kleanthis Avramidis, Richard Leahy +1
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosig…