EEG Based Emotion Prediction with Neural Network Models

The term "emotion" refers to an individual\u27s response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent

Hybrid Classification Model for Emotion Prediction from EEG Signals: A Comparative Study

This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropriate features for classification. The three parallel ANFIS structures were trained using the selected features as input vectors, and the outputs of these models were combined to obtain a new feature vector. This feature vector was then used as the input to the adaptive network, which produced the output of emotion prediction. In addition, it is evaluated the accuracy of the network trained using only the first features of the dataset. The hybrid structure was designed to enhance the system's performance, and the best accuracy result of 96.51% was achieved using the ANFIS-ANFIS model. Overall, this study provides a promising approach for emotion classification based on EEG signals.&nbsp

Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems

Orthogonal frequency division multiplexing (OFDM) combined with multiple input multiple output (MIMO) antennas is one of the promising schemes for high rate data transmission and capacity improvement. However, in these systems, channel estimation task is critical for coherent detection and demodulation. In this study, we have proposed a channel estimator based on radial basis function neural network trained by gradient descent method for MIMO-OFDM systems. Simulation results show that the proposed estimator performs better than other considered channel estimation techniques

CHANNEL ESTIMATION BASED ON NEURAL NETWORK WITH FEEDBACK FOR MIMO OFDM MOBILE COMMUNICATION SYSTEMS

WOS: 000309020100008Multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) has received a great deal of attention of recently in achieving high data rate in wireless communication systems such as WIMAX. Channel estimation is, however, a critical issue for coherent demodulation. In this paper, a new channel estimator based on neural network with feedback for MIMO-OFDM mobile system is designed and its performance is compared to the least square error (LS), least mean square error (LMS), minimum mean square error (MMSE) algorithms and neural network without feedback by using computer simulations. Simulation results demonstrate that our proposed system is an effective solution to channel estimation in time varying fast fading channels without any knowledge of channel statistics and noise information

CHANNEL ESTIMATION BASED ON NEURAL NETWORK WITH FEEDBACK FOR MIMO OFDM MOBILE COMMUNICATION SYSTEMS

Multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) has received a great deal of attention of recently in achieving high data rate in wireless communication systems such as WIMAX. Channel estimation is, however, a critical issue for coherent demodulation. In this paper, a new channel estimator based on neural network with feedback for MIMO-OFDM mobile system is designed and its performance is compared to the least square error (LS), least mean square error (LMS), minimum mean square error (MMSE) algorithms and neural network without feedback by using computer simulations. Simulation results demonstrate that our proposed system is an effective solution to channel estimation in time varying fast fading channels without any knowledge of channel statistics and noise information

Channel estimation based on adaptive neuro-fuzzy inference system in OFDM

In this letter we purpose adaptive neuro-fuzzy inference system (ANFIS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. To evaluate the performance of this estimator, we compare the ANFIS with least square (LS) algorithm, minimum mean square error (MMSE) algorithm by using bit error rate (BER) and mean square error (MSE) criterias. According to computer simulations the performance of ANFIS has better performance than LS algorithm and close to MMSE algorithm. Besides there is unnecessity to send pilot when used the ANFIS

Back Propagation Neural Network Approach for Channel Estimation in OFDM System

In high data rate communication systems which use orthogonal frequency division multiplexing as a modulation scheme, at receiver channel impulse responses must be estimated for coherent demodulation. In this paper, multilayered perceptrons (MLP) neural network with backpropagation (BP) learning algorithm is proposed as a channel estimator for OFDM systems. Our proposed MLP neural channel estimator is compared to least square (LS) algorithm, minimum mean square error (MMSE) algorithm and radial basis function neural network (RBF) in respect to bit error rate (BER) and mean square error (MSE) criteria in order to evaluate the performances. MLP neural network has better performance than LS algorithm and RBF neural network and its performance is close to MMSE algorithm and the perfect channel impulse responses. Moreover, there is unnecessary of channel statistics, matrix computation and noise information when our proposed neural network is used for channel estimation

IMPORTANCE OF THE OCCUPATIONAL HEALTH AND SAFETY EDUCATION AT VOCATIONAL HIGH SCHOOLS (Kirikkale Vocational High School Model)

İş yerlerinde işin yürütülmesi sırasında çeşitli nedenlerden (fiziksel, kimyasal, biyolojik, mekanik, ergonomik, psikososyal) kaynaklanan sağlığa ve güvenliğe zarar verebilecek koşullardan çalışanları ve bulunan diğer üçüncü tarafları korumak amacıyla yapılan sistemli ve bilimsel çalışmalar bütünü olan işçi sağlığı ve güvenliği, gündeme geldiği günden itibaren sürekli gelişme göstermektedir. Bu gelişme süreci içerisinde gerekli düzenlemelerin verimli bir şekilde yapılabilmesi ve bundan sonuç alınabilmesi için öncelikle imalat sektörüne teknik eleman yetiştiren Meslek Yüksek Okullarında müfredat düzeyinde gerekli düzenlemelerin yapılması ve ileride iş yaşamına atılacak öğrencilerin eğitim sürecinde bilinçlendirilmesi gereklidir. Bu çalışmada, işçi sağlığı ve güvenliği konusunda öğrencilerin ve okul yönetiminin konuya yaklaşımı ve öğrencilerin staj yaptıkları işletmelerin konuya verdiği önemi görmek için Kırıkkale Üniversitesi Kırıkkale Meslek Yüksek Okulunda çeşitli teknik programlarda öğrenim gören öğrenciler arasından 240 öğrenciye 15 sorudan oluşan bir anket çalışması uygulanmış ve elde edilen bulgulara göre değerlendirme yapılmıştır. Sonuç olarak işçi sağlığı ve güvenliğinin amacının çalışanları ve üçüncü tarafları, iş kazaları ve meslek hastalıklarından korumak ve önce insan kavramını kazandırmak olduğundan, tüm çalışanlara bu kavramın kazandırılmasında temel eğitimin şart olduğu ve bu eğitimin öğrencilere iş hayatına başlamadan önce öğrenim gördükleri Meslek Yüksek Okullarında verilmesi gerektiği sonucuna varılmıştır.Workplace Health and Safety that is a systematic and scientific body of study aiming to protect employees and third parties from various causes of workplace injuries and hazards (physical, chemical, biological, mechanical, ergonomical, psychosocial) has been improving (developing) since the day it came into question. In this advancing process, to make the necessary regulations in a productive manner and getting favorable results from this, at first, it is required that in vocational faculties in which potential technical staff are educated for the manufacturing sector, necessary regulations in the curriculum be made and awareness of the students entering in the work life in the future be raised. In this study, to appreciate the approach of the students and school administration to this issue and the importance given by the enterprises where students undergo training to this issue, a questionnaire which is consisting of 15 questions was implemented to the 240 students being educated in various types of technical programs in the Vocational Higher Faculty of Kırıkkale University and evaluation was made according to obtained results from the questionnaire. Since eventually the purpose of occupational health and safety is to protect employees and third parties from occupational diseases and industrial injuries and to place the notion of human value first which is a result of basic understanding and training, it is concluded as necessary and important to train students during their education at the vocational high school before they start their work life