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This study introduces a new non-invasive method to measure human intelligence using electroencephalography (EEG) signals. The approach classifies brain activity patterns to directly assess cognitive abilities, offering an alternative to traditional IQ tests.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Electroencephalography (EEG) use is increasing across clinical, psychiatric, and brain-computer interface applications.
  • Quantifying human intelligence using EEG signals has been a long-standing challenge.
  • Traditional IQ tests indirectly measure intelligence via methods like the Wechsler test.

Purpose of the Study:

  • To develop a novel, non-invasive method for measuring human intelligence.
  • To devise a new scoring scheme for quantifying intelligence directly from brain activity.
  • To compare the proposed method with traditional intelligence assessment techniques.

Main Methods:

  • Utilized a 5-channel EEG data acquisition system.
  • Applied Wavelet Packet Transform (WPT) with db-8 mother wavelet for feature extraction.
  • Employed a Hierarchical Extreme Learning Machine (ELM) for EEG signal classification.
  • Conducted power spectral analysis to identify brain regions associated with cognitive tasks.

Main Results:

  • Achieved 80.00% training accuracy and 73.33% testing accuracy for the EEG signal classifier.
  • Reported average sensitivity and specificity of 0.8133 and 0.8923, respectively.
  • Identified distinct EEG band power patterns for memory (theta, beta), arithmetic (alpha, beta), and linguistic (theta, alpha, beta) tasks.

Conclusions:

  • A new direct approach for measuring intelligence by classifying EEG signals is proposed.
  • The method offers a direct assessment of intelligence based on specific brain activities.
  • Findings suggest EEG-based analysis can provide insights into cognitive functions related to memory, arithmetic, and language.