EEG Artifact Removal

The observations of EEG signals are typically corrupted by many physiological effects and electronic/environmental perturbations. The removal of theses artifacts facilitates the physician in proper interpretation of the brain signals. The objective of the proposed research is to explore and identify the best among the existing techniques for artifact removal in EEG. One of the objectives of the project is to develop a real-time artifact removal system that will be employed in our indigenous EEG machines.


The competitive and evolving market trends imply that more and more technological input goes into analyzing how the consumers perceive different products and their promotions. This analysis is useful for the companies to adapt their product promotions in line with the user preferences. Neuromarketing is a recent concept that combines signals from the brain along with the some other markers to analyze response patterns of the users subjected to a visual stimulus. In this regard, EEG sensors allow acquisition of brain signals which are then correlated to other markers for analysis.

P300 and Odd-Ball Paradigm

P300 is an event related potential that is generated by nervous system in response to stimulus.P300 is generated during process of decision making. P300 has its applications in Brain Computer interfacing. P300 speller is type of BCI which uses EEG signals and P300 response evoked by visual stimuli in order to select an item on the computer screen. The goal of this research is to determine whether correct item is selected or not and send feedback for correction if wrong selection has been made.

Classification of Multi-Class Motor Imagery EEG

Brain Computer Interface (BCI) systems enable users to direct commands to an electronic device by using only their brain signals. Motor Imagery (MI) is a technique used to operate BCIs, in which communication with an external device is performed by composing a sequence of mental tasks such as imagination of movement of left and right hand, tongue, or feet. The objective of the project was to find an optimized algorithm for efficient and accurate classification of multi-class MI based EEG signals using limited number of channels.

NeuroInformatics Portal

Data acquisition and sharing is one of the prime hurdles in the progress of neuroinformatics research. Another problem is the access to high computational power that is required to process the huge amount of data. This project endeavors to create a cluster of computational resources available at NUST-SEECS, housing the open-source neural analysis software tools and publically available datasets, providing access to other labs and hospitals who lack this resource. This will allow other researchers to submit their code online and see the results without downloading any data or without requiring any computational strength.