ArticleCognitive neurodynamics2020
A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals.Scientific reports · 2025Article
- Article
- Transcerebral information coordination in directional hippocampus-prefrontal cortex network during working memory based on bimodal neural electrical signals.Cognitive neurodynamics · 2022Article
- Instantaneous mental workload assessment using time-frequency analysis and semi-supervised learning.Cognitive neurodynamics · 2020Article
- Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow.Cognitive neurodynamics · 2020Article
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Authors and funding
5 authors.
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Abstract
We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.
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