Arun Sasidharan

This paper presents an open-source pipeline for EEG-based brain age modeling using the TUEG dataset, evaluating multiple regression models and incorporating SHAP for interpretability.

The paper compares emotion recognition across human faces, emoji faces, and stick figures using behavioral and EEG measures, finding emoji faces are most accurate and fastest, while stick figures show stronger early emotional processing.

This study identifies a reproducible neurodynamic core of meditation in experienced meditators using EEG, distinguishing it from rest and task states with reliable and consistent features.

The paper uses EEG phase slip rates and related biomarkers to identify differences in brain activity between novice Vipassana meditators and non-meditators during decision-making tasks.

The paper analyzes EEG data from meditators and controls to identify neurodynamic features distinguishing meditation techniques from non-meditative states, finding shared yet distinct signatures across traditions.

This study examines non-duality in advanced meditators using EEG-based intrinsic neural timescales and psychological scales, finding that advanced meditators show reduced distinction between internal and external attention.

This study examines the time-to-onset and temporal dynamics of EEG changes during breath-watching meditation across three groups, finding significant differences in power changes between groups.

This study investigates EEG power spectral changes and intersubject correlation during passive listening to three Indian musical modes compared to a control, finding mode-specific effects on brain activity.

This study investigates the neural correlates of equanimity in advanced meditators using EEG data and finds shorter intrinsic neural timescales correlate with higher equanimity.

This paper develops a lightweight autoencoder model for real-time meditation state assessment using EEG data, showing that multivariate EEG features have substantial overlap and temporal dynamics not captured by simple one-class algorithms.

This study investigates brain dynamics in schizophrenia using ERP and fMRI, finding reduced EEG dynamics and altered functional connectivity in specific brain networks during gestalt-perception and corollary-discharge tasks.

This study finds that listening to South Indian classical music with incremental tempo and octave variations reduces anxiety more effectively than stable music or silence in medical students, as measured by EEG and psychological assessments.

This study finds elevated serum adenosine deaminase levels in neuroleptic-naive schizophrenia patients, linked to symptom severity, supporting the adenosine hypothesis.