Evaluation of Mental Workload in Working Memory Tasks with Different Information Types Based on EEG

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Human Factors Engineering

Background:

• Assessing mental workload is crucial for optimizing task performance and preventing cognitive overload.
• Existing methods for mental workload assessment have limitations in real-time, objective measurement.
• Electro-encephalogram (EEG) offers a non-invasive approach to capture brain activity related to cognitive states.

Purpose of the Study:

• To evaluate the efficacy of EEG spectral power and multiscale sample entropy in quantifying mental workload.
• To investigate how these EEG features differentiate workload levels across various N-back tasks (verbal, object, spatial).
• To identify reliable neurophysiological indices for mental workload assessment in different task types.

Main Methods:

• N-back tasks with varying information types and cognitive loads were employed.
• EEG data from 18 adults were recorded and processed using Independent Component Analysis (ICA) for artifact removal.
• Linear (Theta, Alpha power, Theta/Alpha ratio) and nonlinear (multiscale sample entropy) EEG features were extracted and analyzed using MANOVA.

Main Results:

• Increased task load correlated with significant changes in frontal Theta power, Theta/Alpha ratio, and parietal sample entropy.
• Central-parietal Alpha power showed a significant decrease followed by a slight increase with rising task load.
• No significant differences in key EEG markers were observed between verbal and object tasks, or between different spatial tasks.

Conclusions:

• EEG spectral power and multiscale sample entropy are effective indicators of mental workload.
• These neurophysiological measures demonstrate sensitivity to varying cognitive loads in working memory tasks.
• The findings support the use of EEG for objective mental workload assessment across different task modalities.