Comparative Analysis of Feature Extraction Methods for Intelligence Estimation Based on Resting State EEG Data

Engineering Proceedings

article

Tatiana Avdeenko¹  Anastasiia Timofeeva¹  Marina Murtazina¹ 

[1] Applied Mathematics and Computer Science Department, Novosibirsk State Technical University

关键词: EEG;    resting state;    intelligence;    feature extraction;    frequency domain;    connectivity;    graph measures;    principal component regression;   

DOI  :  10.3390/engproc2023033025

来源: mdpi

【 摘 要 】

This paper presents a comparative study of relationship estimation between intelligence indicators and single-channel and multi-channel feature sets extracted from resting EEG data. In the first case, the power of four frequency bands (alpha, theta, beta, delta) calculated using the discrete Fourier transform (DFT) and the power spectral density (PSD) estimated through the Welch’s method for each of the channels were extracted as features from the EEG signals. In the second case, Imaginary Coherence (iMOCH) measure values for a pair of channels in the frequency bands were extracted. Graph theoretical connectivity metrics were calculated for iMOCH. As part of the experimental part of the study, the data of the EEG records of 79 subjects at rest and the values of four IQ indicators (IQ2—ability to abstract; IQ3—verbal analogies and combinatorial abilities; IQ7—figure detecting, combinatorial abilities; IQ8—spatial imagination) of the structure of intelligence were analyzed by the Amthauer method. For relationship estimation, a principal component regression was used. The performance evaluation is based on the nested Monte-Carlo cross-validation. The single-channel feature set provides the smallest standard deviation of mean absolute error. For non-verbal intelligence, the results of the multi-channel approach are better. For verbal intelligence, on the contrary, the single-channel approach gives the best result.

【 授权许可】

Unknown