Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment
Driver’s cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different fr...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1107176/full |
| _version_ | 1797840596621066240 |
|---|---|
| author | Ruiwei Liu Shouming Qi Shouming Qi Siqi Hao Guan Lian Yeying Luo |
| author_facet | Ruiwei Liu Shouming Qi Shouming Qi Siqi Hao Guan Lian Yeying Luo |
| author_sort | Ruiwei Liu |
| collection | DOAJ |
| description | Driver’s cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time–frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver. |
| first_indexed | 2024-04-09T16:17:26Z |
| format | Article |
| id | doaj.art-cb17b57fa61e47f3a1ec4f131172fba0 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-09T16:17:26Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-cb17b57fa61e47f3a1ec4f131172fba02023-04-24T04:19:20ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-04-011410.3389/fpsyg.2023.11071761107176Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experimentRuiwei Liu0Shouming Qi1Shouming Qi2Siqi Hao3Guan Lian4Yeying Luo5Department of Naval Architecture and Marine Engineering, Guangzhou Maritime University, Guangzhou, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, ChinaShenzhen Urban Public Safety and Technology Institute, Shenzhen, Guangdong, ChinaDepartment of Ports and Shipping Management, Guangzhou Maritime University, Guangzhou, ChinaSchool of Transportation and Architecture Engineering, Guilin University of Electronic Technology, Guilin, ChinaDepartment of Ports and Shipping Management, Guangzhou Maritime University, Guangzhou, ChinaDriver’s cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time–frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1107176/fullEEG signalscognitive workloadtime-frequency transformationEEG topography mapevent-related spectral perturbationinter-trial coherence |
| spellingShingle | Ruiwei Liu Shouming Qi Shouming Qi Siqi Hao Guan Lian Yeying Luo Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment Frontiers in Psychology EEG signals cognitive workload time-frequency transformation EEG topography map event-related spectral perturbation inter-trial coherence |
| title | Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment |
| title_full | Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment |
| title_fullStr | Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment |
| title_full_unstemmed | Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment |
| title_short | Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment |
| title_sort | using electroencephalography to analyse drivers different cognitive workload characteristics based on on road experiment |
| topic | EEG signals cognitive workload time-frequency transformation EEG topography map event-related spectral perturbation inter-trial coherence |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1107176/full |
| work_keys_str_mv | AT ruiweiliu usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment AT shoumingqi usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment AT shoumingqi usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment AT siqihao usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment AT guanlian usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment AT yeyingluo usingelectroencephalographytoanalysedriversdifferentcognitiveworkloadcharacteristicsbasedononroadexperiment |