A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison

2018 ◽  
Vol 42 (9) ◽  
Author(s):  
Mohamed Hamada ◽  
B. B. Zaidan ◽  
A. A. Zaidan
Keyword(s):  
Systematic Review ◽  
Feature Extraction ◽  
Group Comparison ◽  
Emotion Classification ◽  
Human Eeg ◽  
Classification Feature ◽  
Brain Signals ◽  
Brain Condition

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

scholarly journals Attention to threat in posttraumatic stress disorder as indexed by eye-tracking indices: a systematic review

2018 ◽  
Vol 49 (5) ◽  
pp. 705-726 ◽  
Author(s):  
Amit Lazarov ◽  
Benjamin Suarez-Jimenez ◽  
Amanda Tamman ◽  
Louise Falzon ◽  
Xi Zhu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Posttraumatic Stress Disorder ◽  
Eye Tracking ◽  
Posttraumatic Stress ◽  
Sustained Attention ◽  
Stress Disorder ◽  
Experimental Studies ◽  
Group Comparison ◽  
Attentional Processes ◽  
Meta Analyses

AbstractBackgroundCognitive models of posttraumatic stress disorder (PTSD) implicate threat-related attentional biases in the etiology and phenomenology of the disorder. However, extant attentional research using reaction time (RT)-based paradigms and measures has yielded mixed results. Eye-tracking methodology has emerged in recent years to overcome several inherent drawbacks of RT-based tasks, striving to better delineate attentional processes.MethodsA systematic review of experimental studies examining threat-related attention biases in PTSD, using eye-tracking methodology and group-comparison designs, was conducted conforming to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Studies were selected following a systematic search for publications between 1980 and December 2017 in PsycINFO, MEDLINE and the National Center for PTSD Research's Published International Literature on Traumatic Stress (PILOTS) database. Additional records were identified by employing the Similar Articles feature in PubMed, and the Cited Reference Search in ISI Web of Science. Reference sections of review articles, book chapters and studies selected for inclusion were searched for further studies. Ongoing studies were also sought through Clinicaltrials.gov.ResultsA total of 11 studies (n = 456 participants in total) were included in the final review. Results indicated little support for enhanced threat detection, hypervigilance and attentional avoidance. However, consistent evidence emerged for sustained attention on threat (i.e. attention maintenance) in PTSD.ConclusionsThis review is the first to systematically evaluate extant findings in PTSD emanating from eye-tracking studies employing group-comparison designs. Results suggest that sustained attention on threat might serve as a potential target for therapeutic intervention.

Emotional Classification Algorithm of Micro-Blog Text Based on the Combination of Emotional Characteristics

2014 ◽  
Vol 1077 ◽  
pp. 246-251
Author(s):  
Bin Yuan ◽  
Tao Jiang ◽  
Hong Zhi Yu
Keyword(s):  
Emotion Classification ◽  
Emotional Information ◽  
Short Text ◽  
Evaluation Standard ◽  
Classification Feature ◽  
Classification Characteristic ◽  
New Type ◽  
Sentiment Orientation ◽  
Parallel Tests ◽  
Emotional Level

Now micro-blog media is growing fast and micro-blog short text has also become a new type of information carrier. User’s sentiment orientation and emotion of the topic or event in a large number of user’s micro-blog, can not only provide decision-making basis in business but also provide support for government's public opinion monitoring. During micro-blog emotion classification, characteristic information is extracted directly influences the classification effect. This paper uses emotional sentences, emotional symbol, emotional word polarity and other emotional information as classification feature, and use NLP&CC Chinese micro-blog sentiment analysis evaluation standard segmentation of emotion in the polarity based emotion. This paper proposed the Chinese micro-blog sentiment classification based on the feature of amorous feeling. Parallel tests suggested that this method has better classification results, and has verified when micro-blog text’s emotional level is higher, the effect of the method is better.

scholarly journals Performance Analysis Between Feature Extraction and Fusion in Familiar and Unfamiliar Typing Biometric Authentication

2021 ◽  
Vol 2071 (1) ◽  
pp. 012041
Author(s):  
I Amalina ◽  
A Saidatul ◽  
C Y Fook ◽  
R F Navea
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Biometric Authentication ◽  
Feature Extraction Method ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Performance Accuracy ◽  
The Mean ◽  
Nonlinear Feature ◽  
The Brain

Abstract The brain signals recorded by EEG devices are largely developed in for biometric authentication purposes. Those signals are very informative and reliable to be classified using signal processing. In this paper, the feature extraction and feature fusion are further studied to observe their performance towards the typing tasks. The signals are pre-processed to eliminate the unwanted noise present in the signals. The feature extraction method such as Welch’s method, Burg’s method and Yule Walk’s method are applied to extract the mean, median, standard deviation and variance in the data. Nonlinear feature such as fuzzy entropy is also been extracted. The extracted features are further classified by using k-Nearest Neighbour (k-NN), Random Forest (RF) and Ensemble Bagged Tree (EBT). The performance of feature extraction and feature fusion through concatenation are recorded and compared. For comparison, the feature fusion shows a better performance accuracy rather than feature extraction. The highest percentage accuracy was produced by Burg’s method for frontal-parietal lobes feature fusion which is 95.94% using Ensemble Bagged Tree (EBT).

scholarly journals Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques

2021 ◽  
Vol 6 (3) ◽  
pp. 056-062
Author(s):  
Dena Nadir George ◽  
Haitham Salman Chyad ◽  
Raniah Ali Mustafa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Image Processing Techniques ◽  
Classification Feature ◽  
Segmentation Methods ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Processing Techniques ◽  
Cancer Diseases

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

scholarly journals An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 2266-2276 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Emotion Recognition ◽  
Speech Development ◽  
Speech Emotion Recognition ◽  
Part Of Speech ◽  
Classification Feature ◽  
The Way

In earlier days, people used speech as a means of communication or the way a listener is conveyed by voice or expression. But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines. With a voice as a bio-metric through use and significance, speech has become an important part of speech development. In this article, we attempted to explain a variety of speech and emotion recognition techniques and comparisons between several methods based on existing algorithms and mostly speech-based methods. We have listed and distinguished speaking technologies that are focused on specifications, databases, classification, feature extraction, enhancement, segmentation and process of Speech Emotion recognition in this paper

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