arXiv subjects

Sayantan Acharya

Publications and source records attributed to Sayantan Acharya.

4 recordsLinked to original sources

arXiv2026

NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress

This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying Partial Directed Coherence (TV-PDC). Unlike conventional EEG classification approaches based on static features, NeuroStrata models the temporal evolution of frequency-specific directed connectivity across distributed brain regions. EEG signals from the 32-channel SAM 40 dataset recorded during mental arithmetic tasks were used to generate TV-PDC connectivity maps. These maps were processed using pretrained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to extract deep connectivity embeddings, which were subsequently classified using lightweight machine learning models. Experimental results demonstrate that beta-band connectivity provides the highest discriminative capability, achieving a peak accuracy of 97.3% using the LAION-CLIP-ViT-L14 backbone with a Support Vector Machine classifier, while alpha-band connectivity exhibits consistently stable performance across model configurations. Connectivity analysis revealed prominent frontal-driven alpha influences and centrally integrated beta connectivity patterns associated with stress-related neural dynamics. Temporal evaluation further indicated that classification performance stabilizes in mid-to-late temporal windows, suggesting progressive consolidation of stress-related connectivity signatures. The proposed framework integrates time-varying effective connectivity modelling with deep representation learning to provide an interpretable and automated approach for EEG-based mental stress analysis.

q-bio.NC↗

arXiv2025

Rewiring Human Brain Networks via Lightweight Dynamic Connectivity Framework: An EEG-Based Stress Validation

In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightweight dynamic brain connectivity framework based on Time Varying Directed Transfer Function is proposed, where TV DTF features were validated through ML based stress classification. TV DTF estimates the directional information flow between brain regions across distinct EEG frequency bands, thereby capturing temporal and causal influences that are often overlooked by static functional connectivity measures. EEG recordings from the 32 channel SAM 40 dataset were employed, focusing on mental arithmetic task trials. The dynamic EEG-based TV-DTF features were validated through ML classifiers such as Support Vector Machine, Random Forest, Gradient Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Experimental results show that alpha-TV-DTF provided the strongest discriminative power, with SVM achieving 89.73% accuracy in 3-class classification and with XGBoost achieving 93.69% accuracy in 2 class classification. Relative to absolute power and phase locking based functional connectivity features, alpha TV DTF and beta TV DTF achieved higher performance across the ML models, highlighting the advantages of dynamic over static measures. Feature importance analysis further highlighted dominant long-range frontal parietal and frontal occipital informational influences, emphasizing the regulatory role of frontal regions under stress. These findings validate the lightweight TV-DTF as a robust framework, revealing spatiotemporal brain dynamics and directional influences across different stress levels.

q-bio.NC↗

arXiv2018

Comparative study of anomalous size dependence of charged and neutral solute diffusion in water

In this work, we perform a comparative study of the size dependence of diffusion of charged and neutral solutes in water. The neutral solute in water shows a nonmonotonicity in the size dependence of diffusion. This is usually connected to the well known Levitation effect where it is found that when solute diffuses through the transient solvent cages then for attractive solute-solvent interaction and for a particular size of the solute there is a force balance which leads to the maximum in diffusion. Similar maximum in diffusion of charged solutes has also been observed and connected to Levitation effect. However, earlier studies of ionic diffusion connects this nonmonotonicity to the interplay between hard sphere repulsion and Coulombic attraction. In this work, we show that although the size dependence of both charged and neutral solutes have a nonmonotonicity, there is a stark difference in their behaviour. For charged solute with increase in attraction the maximum shifts to higher solute sizes and has a lower value whereas for neutral solute it remains at the same place and has a higher value. We show by studying the ionic and non-ionic part of the potential that for larger solutes it is the nonionic part which dominates and for smaller solutes the ionic part and the is a transition between them. As the charge on the solute increases, this transition takes place at larger solute sizes which leads to the shift in the diffusivity maxima and reduction of the peak value. We show that although the charged solutes also explore the solvent cage even before we reach the size which Levitates due to Coulombic attraction the diffusion value drops. Thus the origin of diffusivity maxima in charged and neutral solute diffusion is different.

cond-mat.soft↗

arXiv2017

Fickian yet non-Gaussian behaviour: A dominant role of the intermittent dynamics

We present a study of the dynamics of small solute particles in a solvent medium where the solute is much smaller in size, mimicking the diffusion of small particles in crowded environment. The solute exhibits Fickian diffusion arising from non-Gaussian Van Hove correlation function. Our study shows that there are at least two possible origins of this non-Gaussian behaviour: the decoupling of the solute-solvent dynamics and the intermittency in the solute motion, the latter playing a dominant role. In the former scenario when averaged over time long enough to explore different solvent environments, the dynamics recovers the Gaussian nature. In the case of intermittent dynamics the non-Gaussianity remains even after long averaging and the Gaussian behaviour is obtained at a much longer time. Our study further shows that only for an intermediate attractive solute-solvent interaction the dynamics of the solute is intermittent. The intermittency disappears for weaker or stronger attractions.

cond-mat.soft↗