# Narrowband Theta Investigations for Detecting Cognitive Mental Load

## TL;DR

This study explores how theta band EEG signals can detect cognitive mental load during arithmetic tasks using novel signal analysis methods.

## Contribution

The paper introduces a new signal discriminator inspired by the integral of a function for cognitive load detection.

## Key findings

- EEG activity during mental arithmetic shows distinct behavioral patterns across 64 channels.
- Specific EEG channels were identified as most precise for discriminating cognitive tasks.
- The proposed algorithm effectively detects cognitive load using narrowband theta components.

## Abstract

The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function—specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.