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Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

ABSTRACT: Knowledge of the mental workload induced by a Web page is essential for improving users' browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of mental workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.

SUBMITTER: Jimenez-Molina A

PROVIDER: S-EPMC5855035 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

Jimenez-Molina Angel A Retamal Cristian C Lira Hernan H

Sensors (Basel, Switzerland) 20180203 2

Knowledge of the mental workload induced by a Web page is essential for improving users' browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the ana ...[more]

PMID: 29401688

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Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Project description:IntroductionSurgeons' mental workload during endoscopic sinus surgery (ESS) has not been fully evaluated. The assessment was challenging due to the great diversity of each patient's anatomy and the consequence variety of surgical difficulties. In this study, we examined the mental workload of surgeons with various surgical skill levels during ESS under the standardized condition provided by novel-designed 3D sinus models.Materials and methodsForty-seven participants performed a high-fidelity ESS simulation with 3D-printed sinus models. Surgeons' mental workload was assessed with the national aeronautics and space administration-task load index (NASA-TLX). Associations between the total and subscales score of NASA-TLX and surgical skill index, including the board certification status, the number of experienced ESS cases, and the objective structured assessment of technical skills (OSATS), were analyzed. In addition, 10 registrars repeated the simulation surgery, and their NASA-TLX score was compared before and after the repetitive training.ResultsThe total NASA-TLX score was significantly associated with OSATS score (p = 0.0001). Primary component analysis classified the surgeons' mental burden into three different categories: (1) the skill-level-dependent factors (temporal demand, effort, and performance), (2) the skill-level-independent factors (mental and physical demand), and (3) frustration. After the repetitive training, the skill-level-dependent factors were alleviated (temporal demand; z = -2.3664, p = 0.0091, effort; z = -2.1704, p = 0.0346, and performance; z = -2.5992, p = 0.0017), the independent factors were increased (mental demand; z = -2.5992, p = 0.0023 and physical demand; z = -2.2509, p = 0.0213), and frustration did not change (p = 0.3625).ConclusionSome of the mental workload during ESS is associated with surgical skill level and alleviated with repetitive training. However, other aspects remain a burden or could worsen even when surgeons have gained surgical experience. Routine assessment of registrars' mental burdens would be necessary during surgical training to sustain their mental health.

Dataset Information

Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

Publications

Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

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