Impulsivity Classification Using EEG Power and Explainable Machine Learning

doi:10.1142/S0129065723500065

Return

Impulsivity Classification Using EEG Power and Explainable Machine Learning

2023-01-12

Philippa Hüpen *

Himanshu Kumar

Aliaksandra Shymanskaya

Ramakrishnan Swaminathan

Ute Habel

DOI：10.1142/S0129065723500065

Original

Original request for help

Save

Abstract

En 中文

Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified k-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power (f-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.

Keywords:

Impulsivity

electroencephalography

classification

power

alpha