Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Motivation significantly impacts task performance, but brain activity analysis often requires numerous EEG electrodes, leading to high costs and user burden.
• Existing methods for analyzing motivation's neural correlates are resource-intensive, limiting practical applications where only the motivation effect matters.

Purpose of the Study:

• To develop a cost-effective method for identifying the cognitive motivation effect using a reduced number of EEG electrodes.
• To analyze the relationship between motivation-affected brain areas and task performance to optimize electrode selection.

Main Methods:

• Utilized temporal association rule mining (TARM) to analyze brain activity patterns related to attention and memorization under motivation.
• Applied the artificial bee colony (ABC) algorithm, incorporating the central limit theorem (CLT), to optimize TARM parameters for enhanced accuracy.
• Focused on identifying key electrode locations (FCz and P3) crucial for detecting motivation effects.

Main Results:

• The proposed method successfully identified the cognitive motivation effect using only two EEG electrodes (FCz and P3).
• Achieved an average classification accuracy of 74.5% across individual tests, demonstrating the efficacy of the reduced electrode approach.

Conclusions:

• A reduced-set EEG approach is feasible for detecting cognitive motivation, offering a more practical and economical alternative.
• The TARM and ABC-CLT optimized method provides a reliable way to assess motivation's impact on performance with minimal instrumentation.