Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Abstract

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation

“…Artificial intelligence has been widely used for some years, but it is only recently that researchers started to incorporate it in the psychological and behavioral sciences (DelPozo-Banos et al 2018;Posada-Quintero and Bolkhovsky 2019;Arnold et al 2012). For instance, various tools of artificial intelligence, including machine learning, have been used to analyze electronic health records (DelPozo-Banos et al 2018).…”

Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders

“…In [24], the value varied between 50% and 60% in a three-class classification (with leaveone-subject-out validations), and [23] obtained a value of 75% in the classification using only the EDA (leave-onesubject-out cross validation test). In [30], the accuracy did not exceed 66% in a four-class model with two signals (EDA and HRV) as input attributes (leave-one-subjectout cross-validation). However, these independent subject models do not show results regarding test datasets.…”

“…Statistical analysis [14], [17]- [19], [27] and machine learning [8], [13], [18], [21], [28]- [31] were found as the most-used analysis techniques for the processed signals (to infer the psychological event from the physiological signal). Authors established relationships between EDA/HRV changes and cognitive states such as cognitive load [13], [14], [21], [23], [24], [32], [33], attention [8], [28], memory [19], autistic aspects [17], engagement [20], anxiety states and stress [27], and different cognitive tasks [30] under impairment conditions [31]. There are works recognizing cognition from EEG signals.…”

“…Reasons for using one or the other component are varied and depend primarily on each study's conditions and objectives. However, the SCL component can provide more information to classify different cognitive abilities [30].…”

“…The most common way to identify cognitive states is to measure performance indices from tasks [19], [21], [27], [38], standardized tests [8], [14], [17], [30], [31], self-reports [13], [28], and expert observations [18]. However, these identification strategies are considered subjective and cannot be measured continuously [39].…”