Electroencephalography(EEG) is a seminal electro-physiological method used to record the electrical activity of the brain. These electrical signals arises from populations of neuron which are excitable with intrinsic electrical properties. This technique gives measurement of brain activity at high temporal resolution.
We use EEG to understand how the electrical activity of the brain changes during different attention tasks. We use EGI 128 electrodes acquisition for our experiments. We also have an BioSemi ActiveTwo system with active electrodes for better signal quality.


fMRI (Functional Magnetic Resonance Imaging) is a technique used to measure brain activity in real time. fMRI measures changes in blood flow and and blood oxygenation levels which is associated with brain activity. The technique gives a measurement of brain activity at a very fine spatial scale, and has been widely used to identify areas of the brain associated with different cognitive and behavioral functions.
We use functional MRI data to understand the functional connectivity patterns in the brain, and how these patterns change from task to task.


The eye tracker is a video based instrumennt used to track eye movements through dark pupil and corneal reflection tracking. It uses infra red cameras to detect face, eyes, pupils and corneal reflections to calculate eye movements, gaze direction. The sampling frequency ranges from 30Hz to 1250Hz.



Behaviour is the final manifestation of all neurological processes. It closes the process undergone by the internal neural states, as measured by different modalities like EEG and fMRI, and represents their ultimate tangible output. Consequentially, it is a crucial measure to inform while studying any cognitive phenomena. In our lab, we employ psychophysics techniques to characterise behaviour and parse out its components, and attempt to corroborate all neural and computational findings with supporting behavioural evidence.


Transcranial Magnetic Stimulation is a noninvasive brain stimulation technique that works on the principle of electromagnetic induction. The magnetic flux produced by electricity flowing through a closed coil held perpendicular to the site of stimulation passes through the skull and induces an electrical current directly in the brain tissue. The membrane potentials of the neurons of this region are affected by this current. Thus, it is possible to interfere with the activity of single or multiple brain regions and deduce their causal contributions to cognition.
Transcranial Alternating Current Stimulation (tACS) also enables us to establish causal links between brain activity and behaviour. It allows stimulation for a longer time period. tACS can be used to modulate the brain oscillations associated with attention.


Diffusion Tensor Imaging (DTI) is a widely used non-invasive technique that enables the estimation of white-matter fibres in the brain. This is achieved using measurements of the diffusion of water along these fibres. With the help of simple mathematical concepts and sophisticated algorithms, it is possible to estimate and analyse connectivity patterns between brain regions of interest.


This projects aims at understanding principles of neural computations involved in visuospatial attention by analyzing large scale EEG data. Different brain oscillations have been shown to be correlated with different cognitive activities. In the context of attention, alpha oscillations that occur between 8-12 Hz that occur prominently during states of rest, play an active role in suppressing information irrelevant to the task execution i.e. the distractor information. Steady state visually evoked potentials (SSVEPs) are evoked brain oscillations that occur in response to rhythmically flashing visual stimuli.
In this study, we seek to find the interaction of alpha and SSVEP as well as the interaction of individual signal with stimulus strength and attentional state of the subject.

The brain is capable of exhibiting rich functional dynamics that rests on the foundation of its complex network architecture. Using diffusion MRI, we work towards understanding this and its influence on behaviour and different cognitive functions, in particular, attention and decision- making. We seek to employ the structural connectivity data obtained through dMRI in order to develop a large-scale network model that can effectively predict functional interactions between brain regions. Each node in this network can be modelled using oscillators, whose dynamics can be described by means of coupled differential equations.
Structural changes in the brain are often indicative of neurodegenerative disorders such as Alzheimer’s Disease. dMRI helps us identify structural differences in the connectivity patterns between healthy individuals and patients affected by Alzheimer’s Disease. Further, understanding the structure-function relationship is also key to uncovering differences in brain function that could potentially be arising out of impaired structural connections.

Functional connectivity refers to how different regions of the brain influence each other’s activity. Functional connectivity networks are temporary, and can change from time to time depending on the task the brain is performing.
fMRI is widely used as a technique to identify areas of the brain associated with different cognitive and behavioral functions. However, it is a rich, high-resolution signal that may contain much more information that could be extracted. By fitting mathematical models of connectivity to the data, we can estimate the interaction patterns between different brain regions. Since fMRI has a high spatial resolution but a limited temporal resolution, we are forced to work with very little data of very high dimensionality, which makes this task challenging.
The goal of the project is to build whole-brain functional connectivity networks, and examine how the connectivity patterns change when the subject is performing different attentional tasks. This can lead to crucial insights into the underlying cognitive processes.

Change blindness(CB) is the inability to notice changes that occur in clear view of the observer, even when these changes are large and the observer knows they will occur. (Rensink, 2005). This phenomenon challenges the commonly-held notion that we continuously encode the world around us in its entirety. In this study, we created change blindness stimuli using a flicker paradigm, where the observer views a continuous cycling of displays (two images with a change between them), with a brief blank (mask) after each display (see figure below). The task was to identify the change.
Do people who successfully detect the change in a trial employ different scan strategies, compared to those who don't? What does this tell us about attentional allocation in general? Which regions of the brain might be responsible for the ability to detect changes quickly and accurately? These are some of the questions we seek to answer through this project.

Visuospatial attention may facilitate neural processing of the selected stimulus through two mechanisms: either by influencing perceptual sensitivity or by altering decisional bias. We seek to study the putative causal contributions of areas in the prefrontal cortex, and the posterior parietal cortex to sensitivity and bias modulation during spatial attention. We will apply non-invasive Transcranial magnetic stimulation (TMS) to focally interfere with the activity in these regions. The results will provide important insights into the mechanisms by which these key regions in the frontoparietal cortex, and their associated brain rhythms, contribute to visuospatial selective attention.Similarly, we will be using transcranial alternating current stimulation (tACS) to entrain brain oscillations and understand neural correlates between frontoparietal cortex and selective visual attention.