The Effect of Creative Tasks on Electrocardiogram: Using Linear and Nonlinear Features in Combination with...

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Growing interest in creativity's impact on human life.
• Limited research on the link between creativity and physiological changes.
• Need for objective measures to assess creativity states.

Purpose of the Study:

• To present a novel method for distinguishing creativity states using electrocardiogram (ECG) signals.
• To explore the relationship between cardiac signal features and creativity levels.
• To classify participants into high and low creativity groups based on ECG data.

Main Methods:

• ECG signals were recorded from 52 participants during creative thinking tasks (Torrance Tests of Creative Thinking).
• Linear and nonlinear features were extracted from ECG signals after artifact removal.
• Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed for classification.

Main Results:

• Significant physiological differences (ECG) were observed between rest and creativity tasks.
• SVM achieved high accuracy (99.63%) in distinguishing creativity tasks from rest, especially task 1.
• SVM successfully differentiated high and low creativity groups with 98.41% accuracy.

Conclusions:

• The combination of SVM and linear ECG features effectively reveals physiological changes associated with creativity.
• This approach offers a promising tool for objectively assessing creativity.
• Further research can explore the neurophysiological underpinnings of creativity.