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Stress is a growing concern in this age of technological progress and productivity. Stress is traditionally measured using questionnaires with the help of psychologists. Due to shortage of health facilities and stigma associated with mental checkup, stress measurement process needs to be automated. This paper presents the results of an experiment to predict the perceived stress scores from electroencephalography (EEG) using a linear regression model. EEG signals are recorded using a five channel EEG headband for a duration of three minutes, with dry electrodes. In the first step, we acquire data by the Perceived Stress Scale (PSS) questionnaire and in the second step, we record EEG using muse headband and apply linear regression algorithm. The values of the PSS score serve as ground truth, which is then predicted by linear regression model. From the results, it is shown that the value of root mean squared error is as low as 2.36. This shows that PSS scores can be predicted with considerable accuracy by applying linear regression model on EEG signals. Our research suggests that perceived stress can be estimated from EEG signals using a low-channel headset once the system is trained.

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 summaryTehmina Hafeez ROLES Investigation, Writing – original draft * E-mail: tehminamalik.52@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@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@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@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@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.

Brain computer interface (BCI) establishes a communication channel between a computer and a human brain which converts brain activity to control signals. With the advancement in research and technology, many BCI based rehabilitation devices have been developed to augment, support, and supplement human motion in a paralyzed or partially disabled person. This would help in developing a smart society where a disabled person will have the freedom to complete their day to day tasks. In the proposed experimental setup, brain signals are used to move the robotic arm and perform different tasks i.e., picking and placing. Electroencephalography (EEG) signals are recorded using a five-channel wearable headband. A total of five subjects voluntarily participated in the study, with an informed consent. The EEG data is recorded for a duration of twenty minutes for each participant, and eight different statistical features are extracted to detect clench and attention signals. Five different classifiers namely support vector machine, Naive Bayes, K-nearest neighbor, multilayer perceptron, and random forest are used. The results are compared in terms of accuracy and error parameters. The proposed method achieves significant results for smart robotic arm control.

An exponential increase has been observed in number of mobile game players in last five years. An increasing number of researches have been emerged that assesses the cognitive aspects of video game players. This creates a need to classify the expert-novice level of a player. A novel approach to classify players expert-novice level is to use machine learning algorithm by considering recorded electroencephalography (EEG) signals of player while playing mobile games. In this research work, EEG signals of ten mobile game players is recorded by commercially available 14 channel EMOTIV headset. After preprocessing stage, a feature vector is created in such a way that from each channel, thirteen morphological features are extracted. The extracted features are used to train three different classification algorithms. The Naive Bayes performed with an accuracy of 89:89%. From results, it is evident that EEG can be used to classify the expert-novice level of a player using machine algorithms. These results can be useful in the development of new and interesting entertainment and educational mobile games taking into account the player's cognition using EEG.

This paper present, results of the study on noninvasive stress measurement using EEG signals recorded with a single electrode device. The process involves EEG data acquisition, feature extraction, and stress level classification. Psychologists have developed over a period of time, questionnaires that cover a wide range of symptoms associated with stress. In the first step, stress level of each participant was assessed using the Perceived Stress Scale (PSS) questionnaire. EEG signals of twenty eight participants were recorded using a single channel EEG headset for duration of three minutes. Feature vector based on frequency sub bands is used to train three different machine learning algorithms, to classify the stress level of participants. It is evident from results that psychological stress level can be measured by single channel EEG headset using machine learning algorithms with considerable accuracy. Moreover, increased Beta activity of subjects with high stress has been observed as compared to the subjects with no stress. This fact can be used as a key factor in classifying psychological stress with single channel EEG headset.