scholarly journals EEG in game user analysis: A framework for expertise classification during gameplay

2021 ◽  
Author(s):  
Tehmina Hafeez ◽  
Sanay Muhammad Umar Saeed ◽  
Aamir Arsalan ◽  
Syed Muhammad Anwar ◽  
Muhammad Usman Ashraf ◽  
...  
Keyword(s):  
Computer Science ◽  
Video Game ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Computer Engineering ◽  
K Nearest Neighbor ◽  
Engineering And Technology ◽  
Expertise Level ◽  
E Mail

AbstractVideo 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: [email protected] 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: [email protected] AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Aamir Arsalan ROLES Methodology, Writing – review editing * E-mail: [email protected] AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Syed Muhammad Anwar ROLES Validation, Writing – review editing * E-mail: [email protected] 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: [email protected] 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.

scholarly journals EEG in game user analysis: A framework for expertise classification during gameplay

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0246913
Author(s):  
Tehmina Hafeez ◽  
Sanay Muhammad Umar Saeed ◽  
Aamir Arsalan ◽  
Syed Muhammad Anwar ◽  
Muhammad Usman Ashraf ◽  
...  
Keyword(s):  
Video Game ◽  
Game Design ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
K Nearest Neighbor ◽  
Video Game Design ◽  
Channel Reduction ◽  
Expertise Level ◽  
Diverse Cultures

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).

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

ADAPTIVE SPAM FILTERING USING DYNAMIC FEATURE SPACES

2007 ◽  
Vol 16 (04) ◽  
pp. 627-646 ◽  
Author(s):  
YAN ZHOU ◽  
MADHURI S. MULEKAR ◽  
PRAVEEN NERELLAPALLI
Keyword(s):  
Decision Tree ◽  
Adaptive Learning ◽  
Nearest Neighbor ◽  
Experimental Results ◽  
Huffman Coding ◽  
Support Vector ◽  
Dynamic Feature ◽  
Spam Filtering ◽  
K Nearest Neighbor ◽  
E Mail

Unsolicited bulk e-mail, also known as spam, has been an increasing problem for the e-mail society. This paper presents a new spam filtering strategy that 1) uses a practical entropy coding technique, Huffman coding, to dynamically encode the feature space of the e-mail collected over time and, 2) applies an online algorithm to adaptively enhance the learned spam concept as new e-mail data becomes available. The contributions of this work include a highly efficient spam filtering algorithm in which the input space is radically reduced to a single-dimension input vector, and an adaptive learning technique that is robust to vocabulary change, concept drifting and skewed class distributions. We compare our technique with several existing off-line learning techniques including support vector machine, logistic regression, naïve Bayes, k-nearest neighbor, C4.5 decision tree, RBFNetwork, boosted decision tree and stacking. We demonstrate the effectiveness of our technique by presenting the experimental results on the e-mail data that is publicly available. A more in-depth statistical analysis on the experimental results is also presented and discussed.

scholarly journals Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses

2021 ◽  
Vol 11 (1) ◽  
pp. 106
Author(s):  
Ana R. Andreu-Perez ◽  
Mehrin Kiani ◽  
Javier Andreu-Perez ◽  
Pratusha Reddy ◽  
Jaime Andreu-Abela ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Video Game ◽  
Near Infrared ◽  
Emotional State ◽  
Brain Activity ◽  
Feature Learning ◽  
Video Gaming ◽  
Functional Near Infrared Spectroscopy ◽  
Feature Extraction Method ◽  
Expertise Level

With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers’ cognitive or affective responses. In this work, gamer’s brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video. From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. The best tri-class classification accuracy is obtained using a cascade of random convolutional kernel transform (ROCKET) feature extraction method and deep classifier at 91.44%. This is the first work that aims at decoding expertise level of gamers using non-restrictive and portable technologies for brain imaging, and emotional state recognition derived from gamers’ facial expressions. This work has profound implications for novel designs of future human interactions with video games and brain-controlled games.

scholarly journals Weighted k-Nearest Neighbour for Image Spam Classification

2021 ◽  
pp. 1036-1045
Author(s):  
Ahmad M. Salih ◽  
Ban N. Nadim
Keyword(s):  
Optical Character Recognition ◽  
Data Exchange ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Image Features ◽  
Spam Detection ◽  
K Nearest Neighbor ◽  
Image Spam ◽  
E Mail

E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.

Prediction of Citrullination Sites on the Basis of mRMR Method and SNN

2020 ◽  
Vol 22 (10) ◽  
pp. 705-715 ◽  
Author(s):  
Min Liu ◽  
Guangzhong Liu
Keyword(s):  
Prediction Model ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Protein Sequences ◽  
Support Vector ◽  
Post Translational Modification ◽  
K Nearest Neighbor ◽  
Accurate Identification ◽  
Citrullinated Proteins ◽  
K Nearest Neighbor Algorithm

Background: Citrullination, an important post-translational modification of proteins, alters the molecular weight and electrostatic charge of the protein side chains. Citrulline, in protein sequences, is catalyzed by a class of Peptidyl Arginine Deiminases (PADs). Dependent on Ca2+, PADs include five isozymes: PAD 1, 2, 3, 4/5, and 6. Citrullinated proteins have been identified in many biological and pathological processes. Among them, abnormal protein citrullination modification can lead to serious human diseases, including multiple sclerosis and rheumatoid arthritis. Objective: It is important to identify the citrullination sites in protein sequences. The accurate identification of citrullination sites may contribute to the studies on the molecular functions and pathological mechanisms of related diseases. Methods and Results: In this study, after an encoded training set (containing 116 positive and 348 negative samples) into the feature matrix, the mRMR method was used to analyze the 941- dimensional features which were sorted on the basis of their importance. Then, a predictive model based on a self-normalizing neural network (SNN) was proposed to predict the citrullination sites in protein sequences. Incremental Feature Selection (IFS) and 10-fold cross-validation were used as the model evaluation method. Three classical machine learning models, namely random forest, support vector machine, and k-nearest neighbor algorithm, were selected and compared with the SNN prediction model using the same evaluation methods. SNN may be the best tool for citrullination site prediction. The maximum value of the Matthews Correlation Coefficient (MCC) reached 0.672404 on the basis of the optimal classifier of SNN. Conclusion: The results showed that the SNN-based prediction methods performed better when evaluated by some common metrics, such as MCC, accuracy, and F1-Measure. SNN prediction model also achieved a better balance in the classification and recognition of positive and negative samples from datasets compared with the other three models.

scholarly journals Algorithms for Classification of Signals Derived From Human Brain

2021 ◽  
Vol 15 ◽  
pp. 1521-1526
Author(s):  
Georgi P. Dimitrov ◽  
Galina Panayotova ◽  
Boyan Jekov ◽  
Pavel Petrov ◽  
Iva Kostadinova ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Signal Acquisition ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Non Invasive ◽  
Basic Parameters ◽  
Linear Svm

Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.

scholarly journals Classification of Fish Species with Image Data Using K-Nearest Neighbor

2021 ◽  
Vol 2 (2) ◽  
pp. 54-58
Author(s):  
Kaharuddin Kaharuddin ◽  
Eka Wahyu Sholeha
Keyword(s):  
Computer Science ◽  
Everyday Life ◽  
Fish Species ◽  
Nearest Neighbor ◽  
Image Data ◽  
Test Results ◽  
Shape Features ◽  
K Nearest Neighbor ◽  
The World

Abstract— Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.

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