Analysis of Relationship between Electroencephalograms and Subjective Measurements for In-Vehicle Information System: A Preliminary Study.

Nahyeong Kim,Jungyoon Kim,Suhwan Jung,Muhammad Hussain,Jaehyun Park,Mungyeong Choe,Jungchul Park,Hyun K Kim

doi:10.3390/ijerph182212173

Abstract

In this study, we explored the relationship between objective and subjective measures for usability evaluation in in-vehicle infotainment systems (IVISs). As a case study, four displays were evaluated based on cluster location and display orientation (that is, front–horizontal, front–vertical, right–horizontal, and right–vertical). Thirty-six participants performed tasks to manipulate the functions of the IVISs and data were collected through an electroencephalogram (EEG) sensor and questionnaire items. We analysed a model that estimated EEG-based objective indicators from subjective indicators. As a result, the objective indicators reflected the subjective indicators and were considered to explain the driver’s cognitive state. Although EEG data were collected from only four participants, this study proposed an experimental design that could be applied to the analysis of the relationship between the subject’s evaluation and EEG signals, as a preliminary study. We expect the experimental design and results of this study to be useful in analysing objective and subjective measures of usability evaluation.

Highlights

The in-vehicle display is an infotainment system that conveys necessary information to the driver and provides pleasure
The usability of various in-vehicle displays was evaluated as a case study, and the participants performed tasks using the in-vehicle infotainment systems (IVISs)
Performance-related psychophysiological indices and task completion times were collected as objective indicators, and questionnaire items were collected as subjective indicators

Summary

Introduction

The in-vehicle display is an infotainment system that conveys necessary information to the driver and provides pleasure. When considering the infotainment system, safety is a major concern because multitasking with secondary tasks during driving can cause traffic accidents. The major secondary tasks performed simultaneously with driving include checking driving information and manipulation of the audio, video, and navigation (AVN) systems [1,2]. There have been several studies aimed at minimizing negative effects by studying cognitive conditions using biological indicators. Studies have been conducted to propose different models for predicting driver sickness and fatigue [15,16], and deep-learning-based models for predicting cognitive conditions [17,18,19]. Studies have been conducted to extract and analyse objective indicators based on eye-tracking data, together with usability

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Conclusion