Biology subjects

Gandhi, T. K.

Publications and source records attributed to Gandhi, T. K..

9 recordsLinked to original sources

bioRxiv2026

A Simple Subject Independent Channel Selection in EEG for Motor Imagery Task

Classification of motor imagery (MI) tasks through EEG is valuable in brain-computer interfacing and rehabilitation engineering. EEG channels selection for MI task classification is well discussed problem and is challenging due to its combinatorial nature. Most of the existing methods are subject and task-dependent. This paper introduces a subject-independent EEG channel selection. The proposed approach consists of two stages. First, we rank channels based on their divergence from a reference channel Cz. We hypothesize that channels less divergent from Cz are more relevant for MI task classification. In the second stage, we employ a three-stage feature selection and classification model to evaluate the selected channels. It consists of a bandpass filter, followed by common spatial pattern (CSP) filter and three classifiers viz. SVM, 1-NN and 5-NN. Two publicly available datasets viz. PhysioNet and BCI Competition III IVa datasets have been used to assess the method. It performs 15.21% more than 3Cs and just 2.91% less than all-channels accuracy with as few as 20/118 channels on BCI Competition data and 19.64% more than 3Cs on the PhysioNet dataset with 16/64 channels. Empirical comparison implies that the method performs better than classical models such as CSP Rank, fishers rank, and normalized mutual information, significantly. Results support that our hypothesis that divergence between channels and a reference channel Cz can be used as a ranking measure for channel selection.

neuroscience↗

bioRxiv2026

Classification of Smartphone Interaction Using Multimodal Physiological Signals with a Brain-Body Spatio-Temporal Transformer

Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit qualitatively different cognitive and physiological responses. However, such distinctions is often overlooked by approaches that treat smart-phone use as a monolithic behavior. This paper presents Brain-Body Spatio-Temporal Transformer (BB-STT), a unified deep learning framework for classifying interaction-specific physiological signatures from multimodal signals, including EEG, EDA, PPG, and eye-tracking. BB-STT achieves 83.51% accuracy in distinguishing smartphone from non-smartphone activity and 74.13% accuracy in three-class classification of short-form video, gaming, and baseline viewing. The model demonstrates strong generalization with leave-one-subject-out (LOSO) performance that is also comparable to 5-fold cross-validation accuracy. Cross-modal attention emerges as the key component, improving three-class accuracy by 16.74 points through dynamic integration of multimodal signals. Interpretability analysis indicates a hierarchical organization of physiological responses. Eye-tracking features, particularly gaze depth, enable coarse separation between smartphone and non-smartphone activity. In contrast, finer discrimination between passive video viewing and active gaming on smartphones relies on the joint contribution of bilateral pupil dilation and central EEG features. Together, these results demonstrate the potential of multimodal physiological signals for objective, real-time assessment of digital engagement in naturalistic settings.

neuroscience↗

bioRxiv2026

A multimodal dataset of EEG, eye-tracking, and physiological signals during naturalistic smartphone interactions

Smartphones have become pervasive tools for communication, information consumption, and digital interaction, yet the neurophysiological dynamics associated with naturalistic smartphone use remain insufficiently characterized. Here, we present a multimodal physiological dataset collected during ecologically valid smartphone interaction and a subsequent standardized low-engagement baseline condition. Twenty-three participants engaged with their most frequently used smartphone application (primarily gaming or short form video) for ten minutes, followed by a five-minute passive viewing of a standardized nature video. Simultaneous recordings were obtained from electroencephalography (EEG; 64 channels), wearable eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) sensors. Questionnaire-based assessments, including the smartphone addiction scale (SAS) and the mobile phone problematic use scale (MPPUS), are also collected to characterize individual differences in smartphone-related behavioral traits. All data streams are synchronized using transistor-transistor logic (TTL) trigger signals to ensure precise temporal alignment across modalities. The dataset is organized according to the Brain Imaging Data Structure (BIDS) specification and is publicly available on OpenNeuro (Accession Number: ds007537). This dataset enables the investigation of neural, ocular, and autonomic responses during smartphone interaction and supports multimodal analysis of diverse smartphone behaviors while preserving ecological validity.

neuroscience↗

bioRxiv2026

Personalized smartphone notifications bias auditory salience across processing stages

Modern digital environments expose the brain to a dense stream of personally meaningful cues that differ markedly from the conditions under which sensory systems evolved. Understanding how the brain responds and adapts to such environments is increasingly relevant for cognitive and mental well-being. In this study, we used auditory event-related potentials (ERPs) recorded during a task-irrelevant auditory oddball paradigm to obtain a time-resolved account of neural responses to smartphone notifications. We dissociated the effects of generic notification sounds from the learning-related effects of personalized notification sounds, and examined how these responses varied across levels of smartphone use. Among early stages of processing, generic tones exhibited experience-dependent strengthening of auditory representations, whereas personalized tones engaged stable, pre-learned representations, as reflected in P2 dynamics. The strongest stimulus-dependent modulation was observed in pre-attentive salience detection, reflected in the mismatch negativity (MMN), which was earlier and larger for personalized tones compared to generic ones. At later stages, attentional orienting and extended evaluative processing showed distinct patterns that were differentially sensitive to stimulus identity and smartphone use. Notably, smartphone usage levels modulated later attentional and evaluative stages (P3a and LPP) without altering early pre-attentive prediction-error signaling. Overall, our findings demonstrate that personalized smartphone notifications do not uniformly increase neural distraction. Instead, they engage robust and rapid pre-attentive prediction-error signaling that is largely independent of usage, while habitual smartphone use selectively shapes later attentional orienting and evaluative dynamics. Significance statementModern digital environments are saturated with personalized cues, such as smartphone notifications, that are known to disrupt ongoing behavior. A common assumption in discussions of digital distraction is that repeated exposure to such cues progressively amplifies their sensory salience, rendering habitual users increasingly reactive. Here, we challenge this assumption using an ecologically grounded neural paradigm with personalized notification sounds. We show that personalized notifications engage robust early salience detection that is shared across users, while habitual smartphone use selectively modulates later attentional and evaluative processing. These findings place important constraints on how repeated exposure to personalized digital cues shapes neural processing, indicating that usage-related effects emerge downstream rather than at early sensory stages.

neuroscience↗

bioRxiv2025

Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD Subtypes

BackgroundDifferentiating between mild cognitive impairment (MCI), Alzheimers disease (AD), and frontotemporal dementia (FTD) subtypes remains a clinical challenge due to overlapping cognitive symptoms, structural atrophy, and metabolic patterns, especially in the early stages. Multimodal classification approaches integrating neuroimaging and cognitive scores may offer early and accurate characterization and subsequently improved diagnostic precision. MethodsIn this study, we included 100 participants (50 AD, 30 FTD, including 14 bvFTD and 16 PPA, and 20 MCI) who underwent simultaneous structural MRI and FDG-PET imaging. Cortical thickness (CTH) from anatomical MRI and standardized uptake values from FDG-PET were extracted using FreeSurfer and PETSurfer pipelines, respectively. CTH and FDG-PET features were combined into a single vector through a logistic weighting function derived from ACE-III scores, capturing the progressive nature of cognitive decline in dementia. A Naive Bayes classifier was then trained to differentiate diagnostic groups based on the merged features. ResultsThe model achieved classification accuracies of 83% for MCI vs. dementia (AD + FTD), 85% for MCI vs. FTD, 87% for MCI vs. PPA, 71% for MCI vs. bvFTD, 64% for MCI vs. AD, and 69% for AD vs. FTD. The overall classification accuracy was 68%, with the highest discriminative performance observed in separating MCI from FTD subtypes. ConclusionsThis study presents a novel, cognition-weighted multimodal approach combining structural and metabolic imaging to enhance the classification of neurodegenerative syndromes. Findings from this study, underscore the potential of integrating ACE-III scores with neuroimaging biomarkers for accurate characterization and, early-stage differentiation of MCI, AD, and FTD variants.

neuroscience↗

bioRxiv2025

Neural Correlates of Perceptual Grouping in Brain

Humans are proficient in detecting even subtle regularities in their sensory inputs. In the specific context of vision, one manifestation of this ability is the perception of structures embedded in noisy backgrounds. Here we use electrophysiology to explore the neural correlates of this proficiency. Observers underwent EEG recordings while being presented with random-dot displays. A few dots in some of the displays were arranged in a line. The results reveal that the two kinds of displays, those with and without the embedded lines, though identical in low-level attributes such as mean luminance and spatial frequency distribution, elicit markedly different neural responses. We find that the first divergence in the ERPs corresponding to these two stimulus classes occurs around 100 milliseconds. These differences are evident in occipital sensors suggesting that the perceptual distinction between structured and non-structured stimuli is accomplished in the very early cortical stages of visual processing. The short latency and early locus of the ERP divergence point to a largely feedforward computation underlying this feat of perceptual organization. The functional connectivity for difference between the two events also supports the ERP findings and thus, exhibits relation between synchrony time-course and ERP dynamics.

physiology↗

bioRxiv2023

Distinct rich and diverse clubs regulate coarse and fine binocular disparity processing: Evidence from stereoscopic task-based fMRI

While cortical regions involved in processing binocular disparities have been studied extensively, little is known on how the human visual system adapts to changing disparity magnitudes. Even though there is an established correlation of BOLD signal with disparity magnitudes, this correlation is not inherent and instead arises from specific causal interactions within an integrated network. Here, we investigate causal mechanisms of coarse and fine binocular disparity processing using fMRI with a clinically validated, custom anaglyph-based stimulus. Therefore, we use degree (D) and participation coefficient (PC) metrics representing rich and diverse properties of the brain network, respectively. Twenty-six healthy participants were asked to indicate hidden 3D shapes through anaglyph filters at four disparity magnitudes. Our findings reveal significant changes at different disparity magnitudes in terms of D and PC of Middle Temporal (MT), V2, V3 and Superior Parietal Lobule (SPL) across both hemispheres. Of these, MT exhibited overlapping rich and diverse club characteristics among other brain regions. Further, diverse clubs outperform rich clubs in decoding disparity magnitudes irrespective of the hemisphere, thereby reinforcing their integrative network properties. These findings imply that distinct rich and diverse clubs exist and provide functional evidence for the variability in human stereopsis.

neuroscience↗

bioRxiv2022

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients.

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearmans {rho} = - 0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders. Significance StatementEarly neuroimaging studies have mostly focused on structural MRI imaging to report brain abnormalities in acutely ill COVID-19 patients. It is not clear whether functional abnormalities co-exist with structural alterations in patients who have survived the infection and have been discharged from the hospital. A few recent studies have emerged which attempted to address the structural/functional alterations. However, further investigations across different sites are necessary for more conclusive inference. More importantly, fatigue is a highly prevalent symptom among COVID survivors, therefore, the relations of brain imaging abnormalities to fatigue should be investigated. In this study, we try to address these gaps, by collecting imaging data from COVID survivors, now PCR negative, and healthy subjects from a single site - the Indian Institute of Technology (IIT), Delhi, India. Furthermore, this is a continuation of an ongoing study. We have recently shown structural abnormalities and stronger gray matter volume (GMV) correlates of self-reported fatigue in this group of COVID survivors compared to healthy subjects (Hafiz et al., 2022).

neuroscience↗

bioRxiv2017

Prediction of Epileptic Seizures based on Mean Phase Coherence

Around 1-2 % of the world's population suffers from epilepsy and 20 % of that vulnerable mass can't be cured through surgery or medicine. Epileptic seizures often occur unpredictably and may cause serious damage to the patient in adverse situations, for e.g. getting a seizure while driving or crossing a road can be fatal. In such a scenario, a reliable mechanism to predict the onset of seizure beforehand is much desirable. In this study, A reliable real-time technique for prediction of epileptic seizures is presented. Mean Phase Coherence (MPC), as a measure of phase synchronization, is used to predict impending seizures in a multi-channel EEG data. It was found that during the pre-ictal stages, MPC values and thus phase synchronization between various channels was found to drop significantly below the level in a non-ictal EEG signal. The range of the prediction horizon for seizures varied from 4-10 mins and prediction of impending seizure attack is possible for 8 out of 10 test cases.

neuroscience↗