A Flexible Classifier Based on Optimum Curve Fitting Approach

This study proposes a curve fitting approach for classification problems. The different classification data sets are utilized to test and evaluate the suggested method. For tested classification problems, the Gaussian curve fitting models are used. In the curve fitting stage, the number of curves equals the number of attributes in the related classification problem. For example, there are 4 attributes for iris dataset, thus four Gaussian curves are fitted for this problem. Then, output values of these fitted curves are calculated to average values, and this average value is rounded to the nearest integers. The same procedure is applied to the other dataset with having different number of features. In optimization stage, for each of classification application, the optimum values of constants of Gaussian function are determined by using genetic algorithm. For all used classification dataset, a part of the set is used during the optimization phase, and then the proposed model is validated with the remainder of the dataset. Furthermore, the optimal valuesof each of the attributes in tested classification application are determined by optimization algorithm. It is a valuable property of the proposed method that the accuracy of high classification can be achieved with a low number of reference data by the stage of determination of optimal feature set. Simulation results show that proposed classification approach with optimum values of constants and optimal feature set based on curve fitting has high accuracy rate. The proposed approach can be used for different classification problems

Investigation of the Effect of eSport on HRV Signal by Using Poincar? Plot Analysis

In this work, in order to determination of effect of a puzzle video game on HRV (Heart rate variability) signal, Poincare plot analysis is used. The linear and nonlinear dynamics of HRV were evaluated using HRV recordings obtained from each player at the time of play and rest. For this purpose, we have correlated the Poincar?? plot descriptors with standard HRV measures derived from time-and frequency-domain methods. In addition, the correlations between the descriptors of the Poincar?? plot along with the frequency and time domain measures of HRV have evaluated for the game stage. Statistically significant values were observed in eSport players in gaming and during resting. The obtained results show that play and rest can be separated by using the features obtained from the Poincare plot

Fizyolojik Sinyallerden Bulmaca Video Oyunu Oyuncuları için Etkili Özellik Alt Kümesinin Belirlenmesi

Puzzle video games that look easy to play require effort to score well. As a result, physiological changes can be seen on the players. This study, it is aimed to determine the most effective subset of features that will determine the state of rest and play in individuals. The galvanic skin response (GSR) and heart rate variability (HRV) data were obtained from the volunteers as physiological signals. Here, a total of 9 features were extracted, 4 from GSR and 5 from HRV. Classification successes were determined by creating subsets with different combinations of these features. Experimental results showed that the highest success rate was achieved for the mean and maximum value features extracted from the GSR in determining the game playing state from physiological signals. The study will contribute to future studies on the physical and physiological effects of e-sports games.</p

Evaluation of the EEG Signals and Eye Tracker Data for Working Different N-back Modes

In this study, it is aimed to determine the effects of different modes N-back test which is one of the measurement tools frequently used in measurement of working memory, on the Electroencephalography (EEG) and Eye-tracker data. Eight healthy volunteers participated in this study. The volunteers performed tasks inducing stress and mental fatigue for almost 4 minutes. Each experimental task consists of 72-seconds stress and fatigue inducing 1-position back, 2-position-color and 2-position-image test sessions and three evaluation sessions performed for task. During these sessions, the volunteers were assessed using EEG, Eye Tracker and Visual analogue scale (VAS). VAS was also used to evaluate perceived stress and mental fatigue before and after the N-back test. Power values of EEG signals from volunteers for different test modes were evaluated according to test scores in theta, alpha, beta, and low gamma bands. According to the obtained results, the power values in each subband of the EEG change according to the test mode and the test scores. Beta, alpha and theta frequency bands' power in the frontal cortex (AF7+AF8) increased with then back test score and difficulty level of the game. When the task gets harder, it shows that the heat map of eye tracking is spread over a wider area. The VAS scores for both mental fatigue and stress increased after the N-back test with low and high test scores. According to the graphical results obtained from EEG with subjective evaluation, stress and mental fatigue increased with N-back tests. These results help understanding of the physiological changes of stress and mental fatigue and contribute to improve new approaches to assess stress and mental fatigue

Determination of Dominant EEG Frequency Sub-Bands in the Process of Playing a Puzzle Video Game

The aim of this study was to determine the effective frequency subband of electroencephalography (EEG) signals before and during a computer game. In this study, EEG data obtained from 9 volunteers before and during play were separated using wavelet packet transform and power values were calculated and rest and play situations were classified according to these values. K.Nearest Neighbor algorithm and feed-forward artificial neural network were used in the classification stage. In this study, signals were recorded with four channel mobile EEG device. According to the obtained results, it was determined that the most distinctive situation before and after the game was beta band in AF7 electrode region, low gamma and delta bands in TP9 region and delta band in TP10 region

Assessment of mental fatigue and stress on electronic sport players with data fusion

Stress and mental fatigue are in existence constantly in daily life, and decrease our productivity while performing our daily routines. The purpose of this study was to analyze the states of stress and mental fatigue using data fusion while e-sport activity. In the study, ten volunteers performed e-sport duty which required both physical and mental effort and skills for 2 min. Volunteers' electroencephalogram (EEG), galvanic skin response (GSR), heart rate variability (HRV), and eye tracking data were obtained before and during game and then were analyzed. In addition, the effects of e-sports were evaluated with visual analogue scale and d2 attention tests. The d2 tests are performed after the game, and the game has a positive effect on attention and concentration. EEG from the frontal region indicates that the game is partly caused by stress and mental fatigue. HRV analysis showed that the sympathetic and vagal activities created by e-sports on people are different. By evaluating HRV and GSR together, it was seen that the emotional processes of the participants were stressed in some and excited in others. Data fusion can serve a variety of purposes such as determining the effect of e-sports activity on the person and the appropriate game type

Recovery of facial expressions using functional electrical stimulation after full-face transplantation

Abstract Background We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial emotional expressions of full-face transplantation patients for 5 months. Rehabilitation and analyses were conducted at approximately 3 years after full facial transplantation in the patient group. We report complexity analysis of surface electromyography signals of these two patients in comparison to the results of 10 healthy individuals. Methods Facial surface electromyography data were collected during 6 basic emotional expressions and 4 primary facial movements from 2 full-face transplantation patients and 10 healthy individuals to determine a strategy of functional electrical stimulation and understand the mechanisms of rehabilitation. A new personalized rehabilitation technique was developed using the wavelet packet method. Rehabilitation sessions were applied twice a month for 5 months. Subsequently, motor and functional progress was assessed by comparing the fuzzy entropy of surface electromyography data against the results obtained from patients before rehabilitation and the mean results obtained from 10 healthy subjects. Results At the end of personalized rehabilitation, the patient group showed improvements in their facial symmetry and their ability to perform basic facial expressions and primary facial movements. Similarity in the pattern of fuzzy entropy for facial expressions between the patient group and healthy individuals increased. Synkinesis was detected during primary facial movements in the patient group, and one patient showed synkinesis during the happiness expression. Synkinesis in the lower face region of one of the patients was eliminated for the lid tightening movement. Conclusions The recovery of emotional expressions after personalized rehabilitation was satisfactory to the patients. The assessment with complexity analysis of sEMG data can be used for developing new neurorehabilitation techniques and detecting synkinesis after full-face transplantation