Impulsivity Classification Using EEG Power and Explainable Machine Learning

International Journal of Neural Systems, vol. 33, pp. 2350006

Abstract

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 [Formula: see text]-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 ([Formula: see text]-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.

Authors 5

1. Philippa Hüpen corresponding Aachen Faculty of Medicine Department of Psychiatry, Psychotherapy and Psychosomatics

   RWTH Aachen University · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany

   JARA - Translational Brain Medicine, Aachen, Germany

2. Himanshu Kumar

   Indian Institute of Technology Madras

   Affiliation as printed

   Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India

3. Aliaksandra Shymanskaya Aachen Faculty of Medicine Department of Psychiatry, Psychotherapy and Psychosomatics

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany

4. Ramakrishnan Swaminathan

   Indian Institute of Technology Madras

   Affiliation as printed

   Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India

5. Ute Habel Aachen Faculty of Medicine Department of Psychiatry, Psychotherapy and Psychosomatics

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany

   Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany