scholarly journals Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Nan Zhao
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Music Perception ◽  
Intelligent System ◽  
Recognition Performance ◽  
Brain Wave ◽  
Traditional Classification ◽  
Extraction Model ◽  
Electroencephalogram Eeg ◽  
The Brain

In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Correlation of learning disabilities to porn addiction based on EEG

2021 ◽  
Vol 10 (1) ◽  
pp. 148-155
Author(s):  
Norhaslinda Kamaruddin ◽  
Nurul Izzati Mat Razi ◽  
Abdul Wahab
Keyword(s):  
Direct Analysis ◽  
Learning Disorders ◽  
Intellectual Ability ◽  
Learning Disorder ◽  
Negative Effects ◽  
Brain Wave ◽  
Hyperactivity Disorder ◽  
Psychological Instruments ◽  
Electroencephalogram Eeg ◽  
The Brain

Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.

scholarly journals Study on EEG Feature Extraction under LED Color Exposure to Enhance the Concentration

2011 ◽  
Vol 2-3 ◽  
pp. 261-265 ◽  
Author(s):  
Jung Eun Lim ◽  
Bo Hyeok Seo ◽  
Sun Hyun Kim ◽  
Soon Yong Chun
Keyword(s):  
Feature Extraction ◽  
Task Performance ◽  
Eeg Signals ◽  
Biometric Data ◽  
Brain Wave ◽  
Brain Waves ◽  
Frequency Domains ◽  
The Brain

Since brain waves are expressed in a variety of frequency domains, they were used to analyze a correlation between colors and concentration. In this study, the brain wave reacting when exposed to colors was defined as a color brain wave (CBW). Also the colors on the table were changed during task performance to see colors’ influence on improving concentration and then the brave waves were measured for analysis on and comparison with the findings from the task performance. Based on the biometric data experiment conducted, it was confirmed that the findings during the task performance and those from EEG signals have a correlation and that human’s concentration is thus affected by changes of colors.

scholarly journals EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

2019 ◽  
Vol 8 (6S4) ◽  
pp. 683-688
Keyword(s):  
Feature Extraction ◽  
Cell Activity ◽  
Vital Role ◽  
Brain Cell ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Machine Learning Classification ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
The Brain

Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

scholarly journals Semi-Supervised Generative Adversarial Nets with Multiple Generators for SAR Image Recognition

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2706 ◽  
Author(s):  
Fei Gao ◽  
Fei Ma ◽  
Jun Wang ◽  
Jinping Sun ◽  
Erfu Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Performance ◽  
Sar Image ◽  
Learning Models ◽  
Sar Images ◽  
Stationary Target ◽  
Optical Images ◽  
Supervised Methods ◽  
The Stability

As an important model of deep learning, semi-supervised learning models are based on Generative Adversarial Nets (GANs) and have achieved a competitive performance on standard optical images. However, the training of GANs becomes unstable when they are applied to SAR images, which reduces the feature extraction capability of the discriminator in GANs. This paper presents a new semi-supervised GANs with Multiple generators and a classifier (MCGAN). This model improves the stability of training for SAR images by employing multiple generators. A multi-classifier is introduced to the new GANs to utilize the labeled images during the training of the GANs, which shares the low level layers with the discriminator. Then, the layers of the trained discriminator and the classifier construct the recognition network for SAR images after having been finely tuned using a small number of the labeled images. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) databases show that the proposed recognition network achieves a better and more stable recognition performance than several traditional semi-supervised methods as well as other GANs-based semi-supervised methods.

scholarly journals EEG Signal Analyzing and Simulation Under Computerized Technological Support

2018 ◽  
Vol 7 (3.8) ◽  
pp. 38
Author(s):  
Rohith V ◽  
Prajitha T.V ◽  
Sweety Suresh
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Invasive Technique ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Brain Wave ◽  
Technological Support ◽  
Non Invasive ◽  
The Time Domain ◽  
The Brain

Electroencephalogram (EEG) is a method for acquiring the brain signals for diagnostic purposes. It tracks and records the brain wave patterns. This is a non-invasive technique. The idea behind is to categorize the EEG signal based on the frequency range. The steps include collecting EEG signals, pre-processing, feature extraction, feature selection and classification. The pre-processing eliminates the noises from the signal. EEG signal can be disintegrated by using discrete wavelet transform. The feature extraction methods are used to obtain the time-domain features of the EEG signal. Finally, the classification method determines the variations in the mental state of the person.  

Sign in / Sign up

Export Citation Format

Share Document