A5067171893- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Blind Source Separation Techniques
- Advanced Graph Neural Networks
- Brain Tumor Detection and Classification
- Optical Imaging and Spectroscopy Techniques
- Neural and Behavioral Psychology Studies
- Action Observation and Synchronization
- Olfactory and Sensory Function Studies
- Muscle activation and electromyography studies
- Energy Harvesting in Wireless Networks
- Emotion and Mood Recognition
- Virtual Reality Applications and Impacts
- Health, Environment, Cognitive Aging
- Attention Deficit Hyperactivity Disorder
- Microwave Engineering and Waveguides
- Analog and Mixed-Signal Circuit Design
- Innovative Energy Harvesting Technologies
- Neuroscience and Neural Engineering
- Neural Networks and Applications
École Polytechnique Fédérale de Lausanne
2022-2025
Indraprastha Institute of Information Technology Delhi
2018-2021
Indian Institute of Technology Delhi
2018-2021
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent magnetic resonance imaging (fMRI) studies have demonstrated unique and accurate identification individuals as an accomplished task. However, FC fingerprinting magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG from the Human Connectome Project to assess its...
The discovery that human brain connectivity data can be used as a "fingerprint" to identify given individual from population, has become burgeoning research area in the neuroscience field. Recent studies have identified possibility extract these signatures temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain what extent MEG serve an indicator identifiability during task-related conduct. Here, using naturalistic and...
This article presents a collection of electroencephalographic (EEG) data recorded from 14 participants, that includes 7 participants with Intellectual and Developmental Disorder (IDD) Typically Developing Controls (TDC) under resting-state music stimuli. The EEG were acquired using the EMOTIV EPOC+ device is 14-channel dry electrode device. provides two types data: (1) Raw stimuli (i.e., task based data) (2) pre-processed resting state Alongside this data, we provide robust fully automated...
Intellectual Developmental Disorder (IDD) is a neurodevelopmental disorder involving impairment of general cognitive abilities. This impacts the conceptual, social, and practical skills adversely. There growing interest in exploring neurological behavior associated with these disorders. Assessment functional brain connectivity graph theory measures have emerged as powerful tools to aid research goals. The current contributes by comparing patterns IDD individuals those typical controls....
Abstract Individual characterization of subjects based on their functional connectome (FC), termed “FC fingerprinting”, has become a highly sought-after goal in contemporary neuroscience research. Recent magnetic resonance imaging (fMRI) studies have demonstrated unique and accurate identification individuals as an accomplished task. However, FC fingerprinting magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG from the Human Connectome Project to...
Abstract There has been an emerging interest in the study of functional brain networks cognitive neuroscience order to better understand responses different stimuli. Such studies can help understanding connectivity alterations that arise neurodevelopmental disorders such as intellectual disability (ID). This research contributes this body knowledge by studying ID compared typically developing controls (TDC). Electroencephalography (EEG) data subjects with and TDC is collected through limited...
This paper reports a new dual band-stop filter structure using two cascaded right triangle with distinct capacitors in the excitation gap to ensure resonances. The proposed design represents defected ground (DGS) resonator analyzed by roll-off factor, effective capacitance and inductance. An appropriate equivalent parallel LC circuit model has been developed for performance analysis. A prototype operating at 0.93 GHz 2.44 frequencies on Rogers RO4350B substrate demonstration of effectiveness...
EEG based Brain Computer Interfaces (BCIs) have been extensively researched upon to facilitate healthcare solutions because of their cost-effectiveness, portability, ease use, and non-invasiveness. Among various technologies that can be designed using BCIs, assistive such as orthotics, prosthetics rehabilitative training devices are crucial they aid people with motor disabilities. The pre-requisite for developing accurate BCIs require neuro-feedback corresponding movement perception, imagery...
ABSTRACT Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to identify individuals and decode cognitive tasks. In this work, we use methods from graph structure inference estimate FC, which is in contrast the conventional approach of deriving FC correlation. Furthermore, instead working on raw (temporal) data, infer graphs seed-based co-activation patterns. We also propose a multi-task neural network architecture jointly perform...
Abstract The discovery that human brain connectivity data can be used as a “fingerprint” to identify given individual from population, has become burgeoning research area in the neuroscience field. Recent studies have identified possibility extract these signatures temporal rich dynamics of resting-state magnetoencephalography (MEG) recordings. However, what extent MEG constitute marker identifiability when engaged task-related behavior remains an open question. Here, using naturalistic and...