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Dynamic Causal Modeling (DCM) for EEG Approach to Neuroergonomics

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Neuroergonomics

Part of the book series: Cognitive Science and Technology ((CSAT))

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Abstract

To study the underlying neural mechanisms of human cognitive and physical activities, various neuroimaging techniques such as fMRI, fNIRS, and EEG have been used. Dynamic causal modeling (DCM) for EEG method has gained a considerable amount of attention in recent years for exploring the directional causal relationship between brain areas. Studies using the DCM for EEG method have investigated contemporary neuroergonomics research topics such as mechanisms behind the event-related potential (ERP) components, mismatch negativity (MMN), abnormal coupling of neural regions in neurologically disordered patients, and causal connections supporting motor tasks. This chapter is a good guide for both novice and experienced neuroergonomics researchers as it introduces the motivations behind DCM for EEG and its developmental timelines, as well as explains fundamentals of the DCM for EEG including the neural mass models, state and observer’s equation, and the model selection processes. This chapter also summarizes recent studies that used the DCM for EEG method in the past 10 years in terms of the tasks, preprocessing steps, model construction, and selection. Lastly, recommendations for the future DCM-based neuroergonomics research are presented.

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Authors and Affiliations

  1. Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, 27695-7906, USA

    Jiali Huang & Chang S. Nam

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  1. Jiali Huang
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  2. Chang S. Nam
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Correspondence to Chang S. Nam .

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  1. Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

    Chang S. Nam

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Huang, J., Nam, C.S. (2020). Dynamic Causal Modeling (DCM) for EEG Approach to Neuroergonomics. In: Nam, C. (eds) Neuroergonomics. Cognitive Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-34784-0_8

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