Emotion recognition from EEG signals using back propagation neural network

2019 ◽  
Author(s):  
Rajdeep Ghosh ◽  
Nidul Sinha ◽  
Neetu Singh
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Eeg Signals

scholarly journals Recognition Method of Limb Motor Imagery EEG Signals Based on Integrated Back-propagation Neural Network

2015 ◽  
Vol 9 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Mingyang Li ◽  
Wanzhong Chen ◽  
Bingyi Cui ◽  
Yantao Tian
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Hand Movement ◽  
Recognition Rate ◽  
Back Propagation ◽  
Operation Time ◽  
Back Propagation Neural Network ◽  
Classification Problems ◽  
Eeg Signals ◽  
Elman Neural Network

In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement). According to the motor imagery EEG data categories to be recognized, the IBPNN was designed to consist of 3 single three-layer back-propagation neural networks (BPNN), and every single neural network was dedicated to recognizing one kind of motor imagery. It simplified the complicated classification problems into three mutually independent two-class classifications by the IBPNN. The parallel computing characteristic of IBPNN not only improved the generation ability for network, but also shortened the operation time. The experimental results showed that, while comparing the single BPNN and Elman neural network, IBPNN was more competent in recognizing limb motor imagery EEG signals. Also among these three networks, IBPNN had the least number of iterations, the shortest operation time and the best consistency of actual output and expected output, and had lifted the success recognition rate above 97 percent while other single network is around 93 percent.

Application of Back Propagation Neural Network and Information Entropy in Deep Detection of Anesthesia

2020 ◽  
Vol 10 (8) ◽  
pp. 1875-1879
Author(s):  
Yujuan Zhou ◽  
Lei Wang ◽  
Jintai Jia ◽  
Gema Monasterio
Keyword(s):  
Neural Network ◽  
General Anesthesia ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sample Entropy ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Entropy Index ◽  
Wavelet Entropy ◽  
Anesthesia Depth

In order to study the monitoring of anesthesia depth during general anesthesia, the EEG (electroencephalogram) signals of 30 patients with laparoscopic general anesthesia were taken as the research objects. The approximate entropy, sample entropy, ranking entropy, and wavelet entropy of EEG signals under different anesthesia conditions were compared by BP (Back Propagation) neural network. The results showed that with the deepening of anesthesia, the four kinds of information entropies of EEG signal showed a downward trend. Among them, the sample entropy algorithm, ranking entropy algorithm, and wavelet entropy algorithm had a higher accuracy in the classification of anesthesia depth. Whereas, the network model established by combining sample entropy index and wavelet entropy index had the highest accuracy in judging anesthesia depth, which was 99.98%. To sum up, the method presented to monitor the depth of anesthesia by combining the characteristics of various EEG signals provides a new reference for the monitoring of the depth of anesthesia.

scholarly journals Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

2019 ◽  
pp. 17-20
Author(s):  
Ahmed Abdal Shafi Rasel
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Eeg Signals ◽  
The Neural Network ◽  
Power Spectral ◽  
Eeg Data ◽  
Density Analysis ◽  
Key Features

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropy-based feature extraction is an effective procedure for classifying EEG data.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

Sign in / Sign up

Export Citation Format

Share Document