{"title":"Investigating the relationship between EEG features and N-back task\n difficulty levels with NASA-TLX scores among undergraduate students","authors":"Şeniz Harputlu Aksu, Erman Çakıt, M. Dağdeviren","doi":"10.54941/ahfe1002828","DOIUrl":null,"url":null,"abstract":"For safe and efficient human-machine interactions, the amount of mental\n resources required by the task should not exceed available capacity of the\n person. Therefore, determination of mental workload has critical importance\n in the fields of human factors and ergonomics. Because of its temporal\n dependability, EEG data has become widely used in assessing and measuring\n mental workload in recent years. Accordingly, motivation of the study was to\n examine the role of brain-related data in discriminating mental workload\n levels. The current paper presented a statistically analysis of whether\n pre-determined task difficulty levels led to the intended mental workload\n manipulation. It was aimed to investigate the relationship between EEG\n features, task difficulty levels, subjective self-assessment (NASA-TLX)\n scores and performance measures (accuracy rate and latency). N-back tasks\n have been commonly used in the literature. In this study, n-back memory\n tests were performed at four different difficulty levels. As the number of n\n increases, so does the difficulty of the task. Tests were conducted on 25\n (13 male, 12 female) healthy undergraduate students. The statistical\n analysis was performed for two sets of data. The first dataset, which\n included 300 session-based samples, was conducted in order to examine the\n possible relationship between task difficulty levels, performance criteria,\n and subjective assessments of mental workload. The second dataset, on the\n other hand, was analyzed on the basis of stimulus and consisted of seventy\n EEG features (5 frequency band power for 14 channels) corresponding to\n recording samples. The categorical variable reflecting the difficulty level\n of n-back memory was selected as dependent variable. It was demonstrated how\n the band power varies in various regions of the brain depending on the\n degree of task difficulty. Significant differences between the genders were\n noted in terms of all variables considered. As the task difficulty level\n increased, both the workload perceived by the participants (rho > 0.7, p\n < 0.01) and the latency in response time (rho > 0.6, p < 0.01)\n significantly increased. Otherwise, the correct answer rate decreased as the\n task became more difficult (rho > 0.6, p < 0.01). The number of hits\n (correct answers by detecting the match) was found to be more correlated\n with the task difficulty level compared to number of correct rejects\n (correct answers by detecting the non-match). The workload and its\n sub-dimensions perceived by the participants and performance variables are\n also related to each other. In tests with longer response times,\n participants reported that they felt more workload (rho > 0.6, p <\n 0.01). Conversely, the number of correct answers decreased (rho > 0.6, p\n < 0.01). It was also found that there was a significant difference\n between all difficulty levels compared in pairs, in terms of almost all\n variables (p < 0.001). There was no significant difference between men\n and women in terms of performance measures. However, men were found to\n report higher NASA-TLX scores than women, especially on difficult tasks. As\n a result, significant relationships between data obtained through different\n methods encourage the use of these methods together for reliable analysis in\n future studies.","PeriodicalId":269162,"journal":{"name":"Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54941/ahfe1002828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
For safe and efficient human-machine interactions, the amount of mental
resources required by the task should not exceed available capacity of the
person. Therefore, determination of mental workload has critical importance
in the fields of human factors and ergonomics. Because of its temporal
dependability, EEG data has become widely used in assessing and measuring
mental workload in recent years. Accordingly, motivation of the study was to
examine the role of brain-related data in discriminating mental workload
levels. The current paper presented a statistically analysis of whether
pre-determined task difficulty levels led to the intended mental workload
manipulation. It was aimed to investigate the relationship between EEG
features, task difficulty levels, subjective self-assessment (NASA-TLX)
scores and performance measures (accuracy rate and latency). N-back tasks
have been commonly used in the literature. In this study, n-back memory
tests were performed at four different difficulty levels. As the number of n
increases, so does the difficulty of the task. Tests were conducted on 25
(13 male, 12 female) healthy undergraduate students. The statistical
analysis was performed for two sets of data. The first dataset, which
included 300 session-based samples, was conducted in order to examine the
possible relationship between task difficulty levels, performance criteria,
and subjective assessments of mental workload. The second dataset, on the
other hand, was analyzed on the basis of stimulus and consisted of seventy
EEG features (5 frequency band power for 14 channels) corresponding to
recording samples. The categorical variable reflecting the difficulty level
of n-back memory was selected as dependent variable. It was demonstrated how
the band power varies in various regions of the brain depending on the
degree of task difficulty. Significant differences between the genders were
noted in terms of all variables considered. As the task difficulty level
increased, both the workload perceived by the participants (rho > 0.7, p
< 0.01) and the latency in response time (rho > 0.6, p < 0.01)
significantly increased. Otherwise, the correct answer rate decreased as the
task became more difficult (rho > 0.6, p < 0.01). The number of hits
(correct answers by detecting the match) was found to be more correlated
with the task difficulty level compared to number of correct rejects
(correct answers by detecting the non-match). The workload and its
sub-dimensions perceived by the participants and performance variables are
also related to each other. In tests with longer response times,
participants reported that they felt more workload (rho > 0.6, p <
0.01). Conversely, the number of correct answers decreased (rho > 0.6, p
< 0.01). It was also found that there was a significant difference
between all difficulty levels compared in pairs, in terms of almost all
variables (p < 0.001). There was no significant difference between men
and women in terms of performance measures. However, men were found to
report higher NASA-TLX scores than women, especially on difficult tasks. As
a result, significant relationships between data obtained through different
methods encourage the use of these methods together for reliable analysis in
future studies.