A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

TL;DR

This study explores the effectiveness of EEG signals during various cognitive tasks for early dementia detection, demonstrating that task-based EEG analysis improves classification accuracy over resting-state EEG.

Contribution

It introduces a novel approach by evaluating multiple cognitive tasks and feature extraction methods for EEG-based dementia recognition, providing new insights into task-specific EEG analysis.

Findings

Working memory tasks improve dementia classification accuracy

FBCSP with combined features achieves sensitivity of 0.87

First comprehensive study using multiple cognitive tasks for EEG-based dementia detection

Abstract

In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP). We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.