Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

José Jaime Esqueda-Elizondo,Reyes Juárez-Ramírez,Gilberto Manuel Galindo-Aldana,Oscar Roberto López-Bonilla,Everardo Inzunza-González,Esteban Tlelo-Cuautle,Enrique Efrén García-Guerrero,Laura Jiménez-Beristáin,Alejandra Serrano-Trujillo

doi:10.3390/mca27020021

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

Highlights

This article is an open access articleScientists have always been captivated by the brain, and cognitive processes are the most intriguing for most people
To evaluate the machine learning (ML) models, we rely on the metrics of the Scikit Learn library [49]
The features based on band power spectrum density (PSD), such as Relative Theta Power (RTP), Relative Alpha Power (RAP), Relative Beta Power (RBP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and the TBAR are good features for attention classification

Summary

Introduction

This article is an open access articleScientists have always been captivated by the brain, and cognitive processes are the most intriguing for most people. A fundamental part of these cognitive processes is the attention process. Attention is a cognitive process that enables selecting, focusing on, and sustained information processing [1]. The object of attention can either be an environmental stimulus actively processed distributed under the terms and conditions of the Creative Commons. By sensory systems or associative information and response alternatives generated by the ongoing cognitive activity. This allows us to orient ourselves towards relevant stimuli, ignoring those not, and act . It is the basis of learning, and it is necessary to have it, in order to begin the learning process. There have been many measuring techniques, such as using the response times or the number of clicks given while using particular software, the eye contact time measured from videos, Magnetic Resonance Imaging (MRI) or functional Magnetic Resonance Imaging (fMRI) studies, among other techniques

Objectives

Methods

Results

Discussion

Conclusion