Open Access

ARTICLE

Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

Seth Aishwarya¹, Vaishnav Abeer^1,*, Babu B. Sathish¹, K. N. Subramanya²

1 Department of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, 560059, India
2 Department of Industrial Engineering and Management, R.V. College of Engineering, Bengaluru, 560059, India

* Corresponding Author: Vaishnav Abeer. Email:

Journal of Quantum Computing 2020, 2(4), 157-170. https://doi.org/10.32604/jqc.2020.015018

Received 12 October 2020; Accepted 27 December 2020; Issue published 07 January 2021

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.

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