Identifying predictive EEG features for cognitive overload detection in assembly workers in Industry 4.0

Industry 4.0 will be characterized by far-reaching production automation because of recent advancements in robotics and artificial intelligence. As a consequence, a lot of simple, repetitive assembly tasks will no longer be performed by factory workers, but by machines. However, at the same time, consumers demand more and more personalized products, increasing the need for human assembly workers who can adapt quickly to new and more complex assembly procedures. This need for adaptation is most likely to increase the cognitive workload and potentially overload assembly workers that are used to traditional assembly work tasks. Several studies have tried to identify this cognitive overload in the EEG signal, but many failed because of poor experimental measurement procedures, bad data quality and low sample sizes. In this paper, we therefore designed a highly controlled lab experiment to collect EEG data of a large number of participants (N=46) performing an assembly task under various levels of cognitive load (low, high, overload). This systematic approach allowed us to study which EEG features are particularly useful and valid for cognitive overload assessment in the context of assembly work.

[1]  B. Postle,et al.  The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception , 2015, Current Biology.

[2]  Brendan Z. Allison,et al.  Workload assessment of computer gaming using a single-stimulus event-related potential paradigm , 2008, Biological Psychology.

[3]  K. Kaare,et al.  Smart Health Care Monitoring Technologies to Improve Employee Performance in Manufacturing , 2015 .

[4]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[5]  Ahmed Azab,et al.  Change in Manufacturing – Research and Industrial Challenges , 2012 .

[6]  Keith Case,et al.  Experimental study of cognitive aspects affecting human performance in manual assembly , 2017 .

[7]  Antonio Padovano,et al.  Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context , 2017, Comput. Ind. Eng..

[8]  J. Lisman,et al.  Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. , 2002, Cerebral cortex.

[9]  Jim Nixon,et al.  Measuring mental workload using physiological measures: A systematic review. , 2019, Applied ergonomics.

[10]  S Bonnet,et al.  Efficient mental workload estimation using task-independent EEG features , 2016, Journal of neural engineering.

[11]  J. Gray,et al.  PsychoPy2: Experiments in behavior made easy , 2019, Behavior Research Methods.

[12]  Markus Funk,et al.  Identifying Cognitive Assistance with Mobile Electroencephalography , 2018, Proc. ACM Hum. Comput. Interact..

[13]  Robert J. Gougelet.  Neural Oscillation Dynamics of Emerging Interest in Neuroergonomics , 2019, Neuroergonomics.

[14]  Jelle Saldien,et al.  Understanding mental workload: from a clarifying concept analysis toward an implementable framework , 2018, Cognition, Technology & Work.

[15]  U. Erdmann,et al.  Auditory probe sensitivity to mental workload changes - an event-related potential study. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[16]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[17]  Darryl G. Humphrey,et al.  Assessment of mental workload with task-irrelevant auditory probes , 1995, Biological Psychology.

[18]  Maarten A. Hogervorst,et al.  Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload , 2014, Front. Neurosci..

[19]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[20]  Michele Germani,et al.  A social life cycle assessment methodology for smart manufacturing: the case of study of a kitchen sink , 2017 .

[21]  Peter A Hancock,et al.  State of science: mental workload in ergonomics , 2015, Ergonomics.

[22]  Alan Gevins,et al.  Electroencephalography (EEG) in Neuroergonomics , 2006, Neuroergonomics.

[23]  R. Likert,et al.  The revised Minnesota paper form board test. , 1937 .

[24]  J. Sweller.  COGNITIVE LOAD THEORY, LEARNING DIFFICULTY, AND INSTRUCTIONAL DESIGN , 1994 .

[25]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[26]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[27]  Pavlo D. Antonenko,et al.  Using Electroencephalography to Measure Cognitive Load , 2010 .

[28]  E. Basar,et al.  Oscillatory brain theory: a new trend in neuroscience. , 1999, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[29]  Michael E. Smith,et al.  Neurophysiological measures of cognitive workload during human-computer interaction , 2003 .

[30]  Ding Jinhong,et al.  An event-related potential study of memory encoding , 2003 .

[31]  C. Neuper,et al.  EEG alpha band dissociation with increasing task demands. , 2005, Brain research. Cognitive brain research.

[32]  Chris Berka,et al.  Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset , 2004, Int. J. Hum. Comput. Interact..

[33]  Ying Liu,et al.  A categorical framework of manufacturing for industry 4.0 and beyond , 2016 .

[34]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[35]  F. Thomas Eggemeier,et al.  Workload assessment methodology. , 1986 .

[36]  J. E. Korteling,et al.  Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls , 2015, Front. Neurosci..

[37]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[38]  B. Cain.  A Review of the Mental Workload Literature , 2007 .

[39]  Matthew W. Miller,et al.  A novel approach to the physiological measurement of mental workload. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[40]  Robert Oostenveld,et al.  Estimating workload using EEG spectral power and ERPs in the n-back task , 2012, Journal of neural engineering.

[41]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[42]  Christopher D. Wickens,et al.  Mental Workload: Assessment, Prediction and Consequences , 2017, H-WORKLOAD.

[43]  Wendy Ju,et al.  Beyond dirty, dangerous and dull: What everyday people think robots should do , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[44]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[45]  Rebecca A. Grier,et al.  Fundamental dimensions of subjective state in performance settings: task engagement, distress, and worry. , 2002, Emotion.

[46]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .