7 papers
Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration
Byung-Kwan Ko, Soowon Kim, Seo-Hyun Lee
Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activit…
Confidence-Aware Neural Decoding of Overt Speech from EEG: Toward Robust Brain-Computer Interfaces
Soowon Kim, Byung-Kwan Ko, Seo-Hyun Lee
Non-invasive brain-computer interfaces that decode spoken commands from electroencephalogram must be both accurate and trustworthy. We present a confidence-aware decoding framework…
Imagined Speech State Classification for Robust Brain-Computer Interface
Byung-Kwan Ko, Jun-Young Kim, Seo-Hyun Lee
This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Spe…
Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces
Seo-Hyun Lee, Ji-Ha Park, Deok-Seon Kim
Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to rec…
EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia
Ha-Na Jo, Young-Seok Kweon, Seo-Hyun Lee
This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences sleep disturbances yet does not full…
Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals
Jung-Sun Lee, Ha-Na Jo, Seo-Hyun Lee
Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer int…