arXiv subjects

Min Suk Lee

Publications and source records attributed to Min Suk Lee.

3 recordsLinked to original sources

arXiv2026

Earable Platform with Integrated Simultaneous EEG Sensing and Auditory Stimulation

Conventional scalp-based EEG systems are cumbersome to use, requiring extensive setup, restrictive wiring, and conductive gels that can dry out and limit long-term monitoring, while also carrying social stigma. As a result, there is increasing interest in in-ear EEG technology to improve comfort, convenience, and discretion for users. This work presents a personalized in-ear EEG monitor (IEEM) that simultaneously captures EEG signals from the outer ear while delivering audio playback through the same device. The earpiece is custom-molded to precisely match the user's ear anatomy, providing effective sound isolation from the environment and enabling direct audio transmission into the ear canal. Testing of the assembled earpiece shows successful detection of electrooculography (EOG), eye blinks, jaw clenches, auditory steady-state responses (ASSR), and alpha modulation. Electrochemical impedance spectroscopy (EIS) measurements confirm stable electrode-skin contact, with impedance values similar to those of traditional dry electrodes. The integrated approach enables potential closed-loop neuromodulation applications all in the ear where brain activity can be monitored in real-time and corresponding acoustic stimulation delivered adaptively.

q-bio.NC↗

arXiv2026

Resting-State EEG Biomarkers of Tinnitus Robust to Cross-Subject and Cross-Platform Variation

Tinnitus is a prevalent auditory condition lacking objective biomarkers, motivating the search for reliable neural signatures. EEG, being a noninvasive method of brain imaging with a high temporal resolution provides a way to investigate the neural dynamics that may be associated with tinnitus. The generalizability of EEG-based tinnitus biomarkers across different datasets remains a critical challenge. Microstate theory has allowed for the characterization of quasi-stable topographic configurations in EEG, with some studies reporting altered microstate dynamics in tinnitus patients. This work seeks to improve upon existing dynamical systems analysis and their viability in identifying a robust biomarker. Dynamical features were extracted from two resting-state EEG datasets for the binary classification of tinnitus. Here, robustness is quantified as cross-dataset generalization, which is critical for clinical translation. We employ microstate analysis by identifying topographic states, from which transition probability and state duration features are derived. We also apply Koopman operator analysis through Dynamic Mode Decomposition (DMD) to dimensionality-reduced EEG to extract features in single-window. A linear SVM is trained on each feature set and evaluated in a cross-dataset generalization paradigm. PCA-based Koopman features yield the strongest discrimination metrics across both transfer directions, outperforming microstate-derived features. A Wasserstein-distance consistency analysis further reveals that Koopman eigenvalue \emph{magnitude}, encoding oscillation stability, generalizes across datasets ($\barρ = 0.685$), whereas eigenvalue \emph{phase}, encoding oscillation frequency, does not ($\barρ = 1.583$), providing interpretable evidence that altered oscillatory decay rates, rather than frequency shifts, constitute the more robust tinnitus biomarker.

q-bio.NC↗

arXiv2025

EEG Blink Artifacts Can Identify Read Music in Listening and Imagery

Eye-movement related artifacts including blinks and saccades are significantly larger in amplitude than cortical activity as recorded by scalp electroencephalography (EEG), but are typically discarded in EEG studies focusing on cognitive mechanisms as explained by cortical source activity. Accumulating evidence however indicates that spontaneous eye blinks are not necessarily random, and can be modulated by attention and cognition beyond just physiological necessities. In this exploratory analysis we reanalyze a public EEG dataset of musicians listening to or imagining music (Bach chorales) while simultaneously reading from a sheet of music. We ask whether blink timing in reading music, accompanied by listening or imagery, is sufficient to uniquely identify the music being read from a given score. Intra-subject blink counts and timing are compared across trials using a spike train distance metric (Victor and Purpura, 1997). One-trial-left-out cross-validation is used to identify the music being read with above chance level accuracy (best subject: 56\%, chance: 25\%), where accuracy is seen to vary with subject, condition, and a tunable cost factor for time shifts. Future studies may consider incorporating eye blink contributions to brain decoding, especially in wearables where eye blinks could be easier to record than EEG given their higher amplitudes.

q-bio.NC↗