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Narrowband Theta Investigations for Detecting Cognitive Mental Load
Silviu Ionita1, Daniela Andreea Coman2
1Department of Electronics, Computers and Electrical Engineering, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Arges, Romania.
Sensors (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces a novel method using electroencephalography (EEG) signals to detect cognitive load during mental arithmetic. The findings identify specific EEG channels and signal metrics for accurately distinguishing cognitive task demands.
Area of Science:
- Neuroscience
- Brain-Computer Interface (BCI) Technologies
Background:
- Theta band activity in electroencephalography (EEG) is historically linked to cognitive performance.
- Understanding how EEG signals reflect varying mental task demands is crucial for neuroscience and BCI applications.
Purpose of the Study:
- To develop and test a cognitive load detection algorithm using EEG signals during mental arithmetic tasks.
- To comparatively analyze EEG signal components, focusing on low theta band activity.
Main Methods:
- Collected EEG data from 64 electrodes during mental arithmetic tasks.
- Applied narrowband filtering to extract low theta components from EEG signals.
- Introduced a novel signal discriminator based on the integral of the signal function and used energy for model comparison.
Main Results:
- Developed a cognitive load detection algorithm based on the proposed signal metrics.
- Identified specific EEG channels that are most precise and specific for discriminating cognitive tasks.
- Demonstrated distinct EEG activity patterns across 64 channels during mental tasks.
Conclusions:
- The developed algorithm effectively detects cognitive load induced by arithmetic tests using EEG.
- Specific EEG channels and novel signal metrics show promise for enhancing BCI performance and cognitive state monitoring.
