Quantization-Based Novel Extraction Method Of EEG Signal For Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Ni Putu Dewi Angreni ◽  
Agus Muliantara ◽  
Yuriko Christian
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Process ◽  
Point Of View ◽  
Eeg Signal ◽  
Signal Quantization ◽  
Analysis Process ◽  
Signal Features ◽  
Artificial Neural Network Classifier ◽  
Extraction Pattern

In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, with the result being a feature vector that will be included in the artificial neural network classifier using the Keras library. The experiment carried out is to try to enter quantized and Non-quantized feature vectors into the classifier. As a result, the accuracy of the classification process with the quantization vector was 75%, and the accuracy in the Non-quantized vector classification process was only 58%. These results indicate the EEG signal quantization feature can represent the EEG signal object. Keywords: EEG signal, quantization, DEAP, feature extraction, pattern recognition

scholarly journals Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Amjed S. Al-Fahoum ◽  
Ausilah A. Al-Fraihat
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Eeg Signal ◽  
Property A ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Signal Features ◽  
Frequency Domains ◽  
Auto Regressive ◽  
The Cost

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

scholarly journals Automated handwriting analysis based on pattern recognition: a survey

2021 ◽  
Vol 22 (1) ◽  
pp. 196
Author(s):  
Samsuryadi Samsuryadi ◽  
Rudi Kurniawan ◽  
Fatma Susilawati Mohamad
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Medical Diagnosis ◽  
Forensic Psychology ◽  
Recognition System ◽  
Processing Technique ◽  
Point Of View ◽  
Personality Characteristics ◽  
Classification Model ◽  
Handwriting Analysis

<span>Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. </span>

scholarly journals Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

2019 ◽  
Vol 4 (2) ◽  
pp. 294 ◽  
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Abdullah Hanif
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Human Body ◽  
Extraction Process ◽  
Graphical Form ◽  
Voice Input ◽  
Form Analysis ◽  
Sound Pattern ◽  
Analysis And Design ◽  
Sound Pattern Recognition

Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC

Feature Extraction

2017 ◽  
pp. 48-105
Author(s):  
Anindita Das Bhattacharjee
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Process ◽  
Measured Data ◽  
Data Set ◽  
Content Domain ◽  
Accessibility Problem ◽  
The Common

Accessibility problem is relevant for audiovisual information, where enormous data has to be explored and processed. Most of the solutions for this specific type of problems point towards a regular need of extracting applicable information features for a given content domain. And feature extraction process deals with two complicated tasks first deciding and then extracting. There are certain properties expected from good features-Repeatability, Distinctiveness, Locality, Quantity, Accuracy, Efficiency, and Invariance. Different feature extraction techniques are described. The chapter concentrates of taking a survey on the topic of Feature extraction and Image formation. Here both image and video are considered to have their feature extracted. In machine learning, pattern recognition and in image processing has significant contribution. The feature extraction is one of the common mechanisms involved in these two techniques. Extracting feature initiates from an initial data set of measured data and constructs derived informative values which are non redundant in nature.

FEATURE EXTRACTION IN CHARACTER RECOGNITION WITH ASSOCIATIVE MEMORY CLASSIFIER

1996 ◽  
Vol 10 (04) ◽  
pp. 325-348 ◽  
Author(s):  
MING ZHANG ◽  
CHING Y. SUEN ◽  
TIEN D. BUI
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Associative Memory ◽  
Data Reduction ◽  
Character Recognition ◽  
Recognition System ◽  
Extraction Process ◽  
The Neural Network ◽  
Series Of Experiments

A pattern recognition system mainly contains two functional parts, i.e. feature extraction and pattern classification. The success of such a system depends on not only the effectiveness of each of them, but also their operation in concert. The feature extraction process in a traditional recognition system has two major tasks, namely, to extract deformation invariant signals and to reduce data. When a neural network is used as a pattern classifier, however, an alteration in these basic objectives is needed. In particular, the consideration of data reduction will be replaced by that of the suitability of feature vectors to the neural network. In this paper, feature extraction algorithms in character recognition have been designed based on these principles. The improvements made by these algorithms have been demonstrated in a series of experiments which justify such a change in the fundamental objectives of the feature extraction process when an associative memory classifier is used.

P-Box Theory and SVM Methods with Application in Pattern Recognition

2014 ◽  
Vol 651-653 ◽  
pp. 472-475
Author(s):  
Jia Man Ding ◽  
Yi Du ◽  
Qing Xin Wang ◽  
Ying Jiang ◽  
Lian Yin Jia
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Information Source ◽  
Recognition Rate ◽  
Extraction Process ◽  
Information Loss ◽  
New Method ◽  
Uncertainty Measures ◽  
Different Types ◽  
Traditional Pattern

In order to solve the problem of the information loss on the feature extraction process in the traditional pattern recognition, a new method based on probability boxes theory was proposed. Firstly, the skewness of the fault signal data were used as the information source to construct the tow p-boxes about X and direction. Then, to take advantage of the complementation of the information source, the tow p-boxes from different directions were fused. Finally, the SVM features database was established by extracting different types of cumulative uncertainty measures from p-boxes. The analysis result shows that the combination of p-box and SVM can achieve a high recognition rate, which makes a new way for pattern recognition.

scholarly journals Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

2021 ◽  
Vol 38 (5) ◽  
pp. 1319-1326
Author(s):  
Hidir Selcuk Nogay
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Convolutional Neural Networks ◽  
Extraction Process ◽  
Fingerprint Recognition ◽  
Deep Convolutional Neural Networks ◽  
Fingerprint Pattern ◽  
Comparative Experimental Investigation ◽  
Effective Use

Fingerprint pattern recognition is of great importance in forensic examinations and in helping diagnose some diseases. The automatic realization of fingerprint recognition processes can take time due to the feature extraction process in classical machine learning or deep learning methods. In this study, the effective use of deep convolutional neural networks (DCNN) in fingerprint pattern recognition and classification, in which feature extraction takes place automatically, was examined experimentally and comparatively. Five DCNN models have been designed and implemented with a transfer learning approach. Four of these five models are Alexnet, Googlenet, Resnet-18, and Squeezenet pre-trained DCNN models. The fifth model is the DCNN model designed from the ground up. It was concluded that the designed DCNN models can be used effectively in fingerprint recognition and classification, and that fast results can be obtained and generalized with advanced DCNN models.

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