EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.

By Chris Berka, Daniel J. Levendowski, Michelle N. Lumicao, Alan Yau, Gene Davis, Vladimir T. Zivkovic, Richard Olmstead, Patrice D. Tremoulet, Patrick L. Craven. Aviation Space and Environmental Medicine, 2007

926 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/28a7bad2788637fcf83809cd5725ea847fb74832

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Citations in the corpus

Estimating workload using EEG spectral power and ERPs in the n-back task (2012, background): “…that examined EEG spectral variables next to other physiological variables such as different eye and heart related measures, concluded or suggested EEG to be the most sensitive or promising indicator of workload (Berka et al 2007, Brookings et al 1996, Taylor et al 2010, Christensen et al 2012).”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “There are several precursory studies that derived measures of workload under laboratory conditions (e.g., Gevins et al., 1998; Berka et al., 2007; Sassaroli et al., 2008) or in the context of real operational environments (e.g., Sterman and Mann, 1995; Hankins and Wilson, 1998; Lin et al., 2005),…”
Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, methodology): “[23] found that the electroencephalography (EEG) signal also contained information about the task engagement level and mental workload.”
Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS (2014, background): “Findings in EEG Brouwer et al. (2012); Berka et al. (2007) show that workload induced by the n-back task can be classified in single trial.”
EEG-based classification of positive and negative affective states (2014, methodology): “algorithms [46] in the time-domain EEG signal, such as spikes, amplifier saturation, or excursions that occur during the onset or recovery of saturations.”
Deep learning-based electroencephalography analysis: a systematic review (2019, background): “Applications such as cognitive and affective monitoring are very promising as they could allow unbiased measures of, for example, an individual’s level of fatigue, mental workload, [21, 195], mood, or emotions [5].”
Adding immersive virtual reality to a science lab simulation causes more presence but less learning (2017, methodology): “The workload classi ﬁ er was developed by Berka et al. (2007) using a linear DFA with two classes, low and high mental workload.”
Pay attention!: designing adaptive agents that monitor and improve user engagement (2012, background): “However, recent experiments in neuroergonomics have used electroencephalography (EEG) signals to identify subtle shifts in alertness, attention, and workload in laboratory, simulation, and real-world contexts on a second-by-second timeframe [6].”