@Article{jqc.2020.015018,
AUTHOR = {Seth Aishwarya, Vaishnav Abeer, Babu B. Sathish, K. N. Subramanya},
TITLE = {Quantum Computational Techniques for Prediction of Cognitive State of  Human Mind from EEG Signals},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {157--170},
URL = {http://www.techscience.com/jqc/v2n4/41123},
ISSN = {2579-0145},
ABSTRACT = { The utilization of quantum states for the representation of information 
and the advances in machine learning is considered as an efficient way of modeling 
the working of complex systems. The states of mind or judgment outcomes are 
highly complex phenomena that happen inside the human body. Decoding these 
states is significant for improving the quality of technology and providing an 
impetus to scientific research aimed at understanding the functioning of the human 
mind. One of the key advantages of quantum wave-functions over conventional 
classical models is the existence of configurable hidden variables, which provide 
more data density due to its exponential state-space growth. These hidden 
variables correspond to the amplitudes of each probable state of the system and 
allow for the modeling of various intricate aspects of measurable and observable 
physical quantities. This makes the quantum wave-functions powerful and 
felicitous to model cognitive states of the human mind, as it inherits the ability to 
efficiently couple the current context with past experiences temporally and 
spatially to approach an appropriate future cognitive state. This paper implements 
and compares some techniques like Variational Quantum Classifiers (VQC), 
quantum annealing classifiers, and hybrid quantum-classical neural networks, to 
harness the power of quantum computing for processing cognitive states of the 
mind by making use of EEG data. It also introduces a novel pipeline by logically 
combining some of the aforementioned techniques, to predict future cognitive 
responses. The preliminary results of these approaches are presented and are very 
encouraging with upto 61.53% validation accuracy.},
DOI = {10.32604/jqc.2020.015018}
}