TY - GEN
T1 - EEG signal analysis in 3D modelling to identify correlations between task completion in design user’s cognitive activities
AU - Baig, Muhammad Zeeshan
AU - Kavakli, Manolya
PY - 2018
Y1 - 2018
N2 - Modelling software applications vary from construction to gaming, but learning a modelling software and becoming a skilled user takes a long time and effort. Reducing the time to learn a modelling software is an important topic in human-computer interaction (HCI). To develop futuristic computer-aided design (CAD) systems that require little or no training, it is important to study the user-dependent factors that affect the system performance directly and indirectly by analysing the cognitive activity of the users. In this research, we have presented a new method to segment the EEG data: we segmented designer’s actions and then used it to align with the EEG data, while they draw a 3D object in AutoCAD. We video recorded the design activities and Electroencephalography (EEG) signals while users were performing the task. The mean EEG power of the alpha, beta, theta and gamma bands has been used to analyse the designer behaviour. We found that the users who completed the experiment in a short time-frame were performing more physical actions than perceptual and conceptual actions. Participants with low Completion Time (CT) participants perform 30% more actions per minute than high-CT participants. EEG analysis demonstrated that the task completion time (CT) was negatively correlated with physical actions. Alpha-and beta-band analysis showed that low-CT participants were more comfortable in performing physical action and high-CT participants are relaxed in performing conceptual actions.
AB - Modelling software applications vary from construction to gaming, but learning a modelling software and becoming a skilled user takes a long time and effort. Reducing the time to learn a modelling software is an important topic in human-computer interaction (HCI). To develop futuristic computer-aided design (CAD) systems that require little or no training, it is important to study the user-dependent factors that affect the system performance directly and indirectly by analysing the cognitive activity of the users. In this research, we have presented a new method to segment the EEG data: we segmented designer’s actions and then used it to align with the EEG data, while they draw a 3D object in AutoCAD. We video recorded the design activities and Electroencephalography (EEG) signals while users were performing the task. The mean EEG power of the alpha, beta, theta and gamma bands has been used to analyse the designer behaviour. We found that the users who completed the experiment in a short time-frame were performing more physical actions than perceptual and conceptual actions. Participants with low Completion Time (CT) participants perform 30% more actions per minute than high-CT participants. EEG analysis demonstrated that the task completion time (CT) was negatively correlated with physical actions. Alpha-and beta-band analysis showed that low-CT participants were more comfortable in performing physical action and high-CT participants are relaxed in performing conceptual actions.
KW - Cognitive activity
KW - CAD
KW - EEG
KW - HCI
KW - 3D modelling
UR - http://www.scopus.com/inward/record.url?scp=85059043545&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04224-0_29
DO - 10.1007/978-3-030-04224-0_29
M3 - Conference proceeding contribution
SN - 9783030042233
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 340
EP - 352
BT - Neural Information Processing
A2 - Cheng, Long
A2 - Leung, Andrew Chi Sing
A2 - Ozawa, Seiichi
PB - Springer
CY - Cham
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
Y2 - 13 December 2018 through 16 December 2018
ER -