Leveraging Artificial Immune Systems for Mental Health Research: Anomaly Detection in EEG Data

Abstract

Mental Health is a physical, mental, and social state affecting 970 million people in the world. Artificial Intelligence and deep learning techniques classifying ElectroEncephaloGraphy (EEG) data have emerged as a promising technology for the detection of mental health disorders. In this context, one underexplored area is the application of Artificial Immune Systems, which is a technique inspired by the human immune system that has been useful in many computational tasks, including anomaly detection. This paper aims to bridge the gap by leveraging Artificial Immune Systems for Mental Health through anomaly detection in EEG Data: a novel Negative Selection Clonal for Anomaly Detection (NSCAD) algorithm is presented and applied on a dataset of 945 samples with individuals diagnosed with disorders and a control group of healthy participants. Efficacy of NSCAD on anomaly detection was assessed using precision, recall, F1-score, and accuracy metrics. Results are promising, with a precision of 0.92, a recall of 0.83, an F1-score of 0.88, and an accuracy of 0.78. A comparative analysis between the evaluation metrics and anomaly detection of NSCAD vs other methods is finally reported together with a critical analysis of the limitations.

Received: 18 October 2024 | Revised: 7 April 2025 | Accepted: 24 April 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Shivani Bhandari: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Neil Buckley: Conceptualization, Methodology, Formal analysis, Resources, Writing – original draft, Visualization, Supervision, Project administration. Emanuele Lindo Secco: Resources, Writing – review & editing, Visualization.