1 citations · 2 across the 9 of their papers we have counts for
9 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…
Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia
Eunyeong Ko, Soowon Kim, Ha-Na Jo
Individuals with aphasia experience severe difficulty in real-time verbal communication, while most imagined speech decoding approaches remain limited to offline analysis or comput…
Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding
Ji-Ha Park, Seo-Hyun Lee, Soowon Kim +1
Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. A…
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models
Soowon Kim, Ha-Na Jo, Eunyeong Ko
In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varyin…
Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface
Ji-Ha Park, Seo-Hyun Lee, Soowon Kim +1
Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an in…
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks
Seo-Hyun Lee, Young-Eun Lee, Soowon Kim +3
Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Ne…
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals
Young-Eun Lee, Seo-Hyun Lee, Soowon Kim +3
Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performan…
Brain-Driven Representation Learning Based on Diffusion Model
Soowon Kim, Seo-Hyun Lee, Young-Eun Lee +3
Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. D…