A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments

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...

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Bibliographic Details
Published in:Cognitive neurodynamics 2020-06, Vol.14 (3), p.301-321
Main Authors: Mora-Sánchez, Aldo, Pulini, Alfredo-Aram, Gaume, Antoine, Dreyfus, Gérard, Vialatte, François-Benoît
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biochemistry
Biomarkers
Biomedical and Life Sciences
Biomedicine
Brain
Cognition & reasoning
Cognitive ability
Cognitive load
Cognitive Psychology
Communication
Computer applications
Computer Science
Confounding (Statistics)
Discriminant analysis
EEG
Electroencephalography
Feedback
Human-computer interface
Implants
Mathematics
Memory
Neurosciences
Phonology
Real time
Research Article
Short term memory
Speech
Time
Visual tasks
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Summary: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.
ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-020-09573-x