1 citations · 6 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Teager-Kaiser Energy Methods For EEG Feature Extraction In Biomedical Applications
Ioanna Chourdaki, Kleanthis Avramidis, Christos Garoufis +2
Electroencephalography (EEG) signals are inherently non-linear, non-stationary, and vulnerable to noise sources, making the extraction of discriminative features a long-standing ch…
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…
Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
Jihwan Lee, Aditya Kommineni, Tiantian Feng +4
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework…
Evaluating Atypical Gaze Patterns through Vision Models: The Case of Cortical Visual Impairment
Kleanthis Avramidis, Melinda Y. Chang, Rahul Sharma +2
A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime examp…
Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients
Kleanthis Avramidis, Kranti Adsul, Digbalay Bose +1
This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic pati…