Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures

Irina E Nicolae,Mihai Ivanovici

doi:10.3390/app11094306

Abstract

In practical applications, such as patient brain signals monitoring, a non-invasive recording system with fewer channels for an easy setup and a wireless connection for remotely monitor physiological signals will be beneficial. In this paper, we investigate the feasibility of using such a system in a visual perception scenario. We investigate the complexity perception of color natural and synthetic fractal texture images, by studying the correlations between four types of data: image complexity that is expressed by computed color entropy and color fractal dimension, human subjective evaluation by scoring, and the measured brain EEG responses via Event-Related Potentials. We report on the considerable correlation experimentally observed between the recorded EEG signals and image complexity while considering three complexity levels, as well on the use of an EEG wireless system with few channels for practical applications, with the corresponding electrodes placement in accordance with the type of neural activity recorded.

Highlights

In the last decades, monitoring human brain activity has shown promising potential in helping us better understand the functioning of our brain, with implications in human health by preventing mental disorders and cognitive decline, improving the quality of life
Considering human interpretation when expressing and analyzing texture is of great importance, since these aspects can drastically influence the complexity perception, as we will see in the following analysis Section 7.2
This paper is a follow up of the investigations carried out in [48], and it contributes to the understanding of the relation between visual complexity perception and the computational visual complexity expressed by image analysis and it provides the directions for multi-scale studies with a higher number of participants and more types of textures

Summary

Introduction

In the last decades, monitoring human brain activity has shown promising potential in helping us better understand the functioning of our brain, with implications in human health by preventing mental disorders and cognitive decline, improving the quality of life. In such scenarios of clinical environments, ambulatory circumstances and further in monitoring various everyday activities, the flexible non-invasive surface EEG measure is predominant for studying the dynamics of the brain, measuring real-life interactions of humans with their environment. Frequent advances [20,21] are taking steps in this regard to help analyze and interpret the neural data with progressive precision

Methods

Results

Discussion

Conclusion