BCI for a brain state control in a dual-task paradigm

Maksimenko, Vladimir · 2019 · Crossref

DOI: 10.35470/2226-4116-2019-8-4-262-266

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Summary

This study addresses the challenge of monitoring cognitive performance during dual-task scenarios, where limited brain resources must be distributed between a primary task and a distracting secondary task. The authors propose a passive brain-computer interface (BCI) to detect real-time changes in cognitive performance when a subject performs a resource-demanding visual classification task alongside a mental arithmetic task. The motivation stems from the need to understand how additional tasks reduce the resources allocated to a main task, leading to performance decrements, and to develop a method for quantifying this shift in cognitive load. The experimental design involved ten healthy subjects (ages 20–28) who participated in three consecutive four-minute sessions. The main task required the binary classification of bistable Necker cube stimuli, while the additional task involved sequential mental subtraction. Session 1 consisted of the main task only, Session 2 combined both tasks, and Session 3 returned to the main task alone. Electroencephalography (EEG) data were recorded from five electrodes (O1, O2, P3, P4, Pz) using a 250-Hz sampling rate. The BCI algorithm estimated brain response amplitude by analyzing the ratio of spectral power in the beta (15–30 Hz) and alpha (8–12 Hz) frequency bands. Specifically, the system calculated the difference in spectral components before and after stimulus onset, hypothesizing that high cognitive performance correlates with increased beta activity and decreased alpha activity in occipital and parietal regions. The results demonstrated that the mean brain response amplitude significantly decreased during Session 2, when the additional mental arithmetic task was performed concurrently with the visual task. This decrease was statistically significant (p < 0.05 via Wilcoxon test) compared to Session 1. In Session 3, the brain response amplitude recovered to levels comparable to Session 1, indicating that the performance drop was not due to mental fatigue or training effects but was directly caused by the reallocation of cognitive resources to the additional task. The study confirmed that the BCI could detect this reduction in cognitive performance within a short time interval of less than 30 seconds. The significance of this work lies in the development of a passive BCI capable of real-time monitoring of cognitive state changes in dual-task paradigms. By linking specific EEG spectral changes—synchronization of beta rhythms and desynchronization of alpha rhythms—to cognitive load, the system provides a non-invasive method for assessing attention and performance. This approach has implications for applications requiring sustained attention, where detecting distractions or task-switching-induced performance drops is critical for maintaining safety and efficiency.

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• neuro workload indices