Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players’ brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player’s expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users’ attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of $88\%$ , when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

The rise in cyber security issues has caused significant harm to tech world and thus society in recent years. Intrusion detection systems (IDSs) are crucial for the detection and the mitigation of the increasing risk of cyber attacks. False and disregarded alarms are a common problem for traditional IDSs in high-bandwidth and large-scale network systems. While applying learning techniques to intrusion detection, researchers are facing challenges mainly due to the imbalanced training sets and the high dimensionality of datasets, resulting from the scarcity of attack data and longer training periods, respectively. Thus, this leads to reduced efficiency. In this research study, we propose a strategy for dealing with the problems of imbalanced datasets and high dimensionality in IDSs. In our efficient and novel framework, we integrate an oversampling strategy that uses Generative Adversarial Networks (GANs) to overcome the difficulties introduced by imbalanced datasets, and we use the Random Forest (RF) importance algorithm to select a subset of features that best represent the dataset to reduce the dimensionality of a training dataset. Then, we use three deep learning techniques, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to classify the attacks. We implement and evaluate this proposed framework on the CICIDS2017 dataset. Experimental results show that our proposed framework outperforms state-of-the-art approaches, vastly improving DL model detection accuracy by 98% using CNN.

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals.We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help provide an optimal gaming experience level by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert/novice players’ brain activity is different, which can be classified using the frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach identifies two significant EEG channels, i.e., AF3 and P7, from the Emotiv EPOC headset’s fourteen channels. The features extracted from these EEG channels contribute the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying the game player’s expertise level with up to 98.04% classification accuracy. Author summary Tehmina Hafeez ROLES Investigation, Writing – original draft * E-mail: tehminamalik.52{at}gmail.com AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan. Sanay Muhammad Umar Saeed (Corresponding author) ROLES Conceptualization, Writing – review editing * E-mail: sanay.muhammad{at}uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan. Aamir Arsalan ROLES Methodology, Writing – review editing * E-mail: aamir.arsalan{at}uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan. Syed Muhammad Anwar ROLES Validation, Writing – review editing * E-mail: s.anwar{at}uettaxila.edu.pk AFFILIATION Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan. Muhammad Usman Ashraf (Corresponding author) ROLES Validation, Writing – review editing * E-mail: usman.ashraf{at}skt.umt.edu.pk AFFILIATION Department of Computer Science, University of management and Technology, Lahore (Sialkot), 51040, Pakistan. Khalid Alsubhi ROLES Conceptualization, Writing – review editing AFFILIATION Department of Computer Science, King Abdul Aziz University, Jeddah, Saudi Arabia.