Ha-Na Jo

Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak et al.

Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine, essential for identifying various sleep disorders. By capturing physiological signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a patient's sleep architecture. However, its dependency on complex equipment and expertise confines its use to specialized clinical settings. Addressing these limitations, our study aims to perform PSG by developing a system that requires only a single EEG measurement. We propose a novel system capable of reconstructing multi-signal PSG from a single-channel EEG based on a masked autoencoder. The masked autoencoder was trained and evaluated using the Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the similarity between original and reconstructed signals. The model demonstrated proficiency in reconstructing multi-signal data. Our results present promise for the development of more accessible and long-term sleep monitoring systems. This suggests the expansion of PSG's applicability, enabling its use beyond the confines of clinics.

Eunyeong Ko, Soowon Kim, Ha-Na Jo

A diffusion-based neural decoding framework optimized for real-time imagined speech classification in individuals with aphasia. The system integrates a lightweight conditional diffusion encoder and convolutional classifier trained using subject-specific EEG data acquired from a Korean-language paradigm. A dual-criterion early stopping strategy enabled rapid convergence under limited calibration data, while dropout regularization and grouped temporal convolutions ensured stable generalization. During online operation, continuous EEG streams were processed in two-second sliding windows to generate class probabilities that dynamically modulated visual and auditory feedback according to decoding confidence. Across twenty real-time trials, the framework achieved 65% top-1 and 70% top-2 accuracy, outperforming offline evaluation (50% top-1). These results demonstrate the feasibility of deploying diffusion-based EEG decoding under practical clinical constraints, maintaining reliable performance despite environmental variability and minimal preprocessing. The proposed framework advances the translation of imagined speech brain-computer interfaces toward clinical communication support for individuals with severe expressive language impairment.

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 interface technology leverages this brain activity to generate external commands for controlling the environment, offering critical advantages to individuals with paralysis or locked-in syndrome. Within the brain-computer interface domain, brain-to-speech research has gained attention, focusing on the direct synthesis of audible speech from brain signals. Most current studies decode speech from brain activity using invasive techniques and emphasize spoken speech data. However, humans express various speech states, and distinguishing these states through non-invasive approaches remains a significant yet challenging task. This research investigated the effectiveness of deep learning models for non-invasive-based neural signal decoding, with an emphasis on distinguishing between different speech paradigms, including perceived, overt, whispered, and imagined speech, across multiple frequency bands. The model utilizing the spatial conventional neural network module demonstrated superior performance compared to other models, especially in the gamma band. Additionally, imagined speech in the theta frequency band, where deep learning also showed strong effects, exhibited statistically significant differences compared to the other speech paradigms.