Research Article Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset Nazmus Sakib , 1,2 Md Kaful Islam , 1,2 and Tasnuva Faruk 2,3 1 Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh 2 Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh 3 Department of Public Health, Independent University Bangladesh (IUB), Dhaka, Bangladesh Correspondence should be addressed to Md Kaful Islam; kaful_islam@iub.edu.bd Received 21 January 2023; Revised 9 April 2023; Accepted 17 April 2023; Published 31 May 2023 Academic Editor: Abdul Rehman Javed Copyright © 2023 Nazmus Sakib et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data fltered at diferent band frequencies were applied to KNN and SVM classifers with diferent kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifer. And with the same features and classifer overall accuracy=98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specifcity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the fndings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method. 1. Introduction Depressive disorder is a highly prevalent mental illness. Sadness, loss of interest or enjoyment, feelings of guilt or low self-worth, interrupted sleep or food, fatigue, and difculty concentrating are some characteristics of depression. It may afect a person’s capacity to operate in daily life or at work or school. According to the World Health Organization (WHO) back in 2015, almost 4.4% of the world’s population was sufering from depression [1]. Because of the COVID-19 pandemic, many people sufered from depression due to job loss, study hampering, losing close relatives, staying indoors, etc. A study showed 19.3% increase in depression symptoms among people after COVID-19 in the United States [2]. A study has shown the changes in obsession, depression, and quality of life in schizophrenia patients before and after COVID-19 [3]. When depression is severe it can lead to suicide. Every year around 800 thousand people die because of suicide [1]. In 2017, 13.2% of young adults (aged 18–25) in the U.S. sufered from depression which was 5.1% less in the year 2009 [4]. Of the deaths of young people, around 9.1% are due to suicide [5]. In most suicide cases, people had psychiatric disorders where depression is the most common disorder among others [6]. According to a recent study, insecure attachment styles are linked to greater problems such as depression, social anxiety, and suicidal thoughts [7]. So, depression is a major issue that should be diagnosed and treated at an early age to prevent suicide and for the bet- terment of the quality of life. Tere are various screening tools to detect depression. Tere are chronic social defeat stress models of depression such as the Morris water maze test and T-maze test to learn about the cognitive functions [8]. Traditionally, clinical questionnaire-based diagnoses are used to detect depression, Hindawi Computational Intelligence and Neuroscience Volume 2023, Article ID 1701429, 23 pages https://doi.org/10.1155/2023/1701429