scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

ekspresi Mengidentifikasi Mood Mahasiswa Berdasarkan Ekspresi Wajah dengan Menggunakan Discrete Wavelet Transform dan Fuzzy K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
pp. 34-38
Author(s):  
Nur Inzani Reski Amalia ◽  
Jayanti Yusmah Sari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor

Mood adalah keadaan emosional yang bersifat sementara. Mood biasanya memiliki nilai kualitas positif atau negatif. Kecerdasan emosional memiliki peran lebih dari 80% dalam mencapai kesuksesan hidup dan menjadi salah satu faktor yang mempengaruhi daya tangkap mahasiswa dalam proses perkuliahan. Dengan mengetahui emosi-emosi mahasiswa, kita dapat membantu daya tangkap mahasiswa saat proses perkuliahan, serta dibutuhkannya sistem yang dapat mengidentifikasi emosi-emosi yang terbentuk saat perkuliahan berlangsung. Sistem ini dibangun menggunakan  Discrete Wavelet Transform yang mentransformasikan citra menjadi 4 sub-image. Citra hasil Discrete Wavelet Transform tampak kasar atau membentuk wajah yang dapat membedakan ekspresi mahasiswa. Hasil pengolahan citra Discrete Wavelet Transform di klasifikasikan dengan menggunakan Fuzzy K-nearest neighbor. Pengklasifikasian dibagi kedalam tiga ekspresi yaitu : Marah, Senang dan Sedih dengan akurasi 77,49%

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

AUTOMATED CHARACTERIZATION OF CARDIOVASCULAR DISEASES USING WAVELET TRANSFORM FEATURES EXTRACTED FROM ECG SIGNALS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940009 ◽  
Author(s):  
AHMAD MOHSIN ◽  
OLIVER FAUST
Keyword(s):  
Wavelet Transform ◽  
Computer Program ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Disease Diagnosis ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Ecg Signals

Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support that claim, we created a computer program that recognizes normal, Dilated Cardiomyopathy (DCM), Hypertrophic Cardiomyopathy (HCM) or Myocardial Infarction (MI) ECG signals. The computer program combined Discrete Wavelet Transform (DWT) based feature extraction and K-Nearest Neighbor (K-NN) classification for discriminating the signal classes. The system was verified with tenfold cross validation based on labeled data from the PTB diagnostic ECG database. During the validation, we adjusted the number of neighbors [Formula: see text] for the machine learning algorithm. For [Formula: see text], training set has an accuracy and cross validation of 98.33% and 95%, respectively. However, when [Formula: see text], it showed constant for training set but dropped drastically to 80% for cross-validation. Hence, training set [Formula: see text] prevails. Furthermore, a confusion matrix proved that normal data was identified with 96.7% accuracy, 99.6% sensitivity and 99.4% specificity. This means an error of 3.3% will occur. For every 30 normal signals, the classifier will mislabel only 1 of the them as HCM. With these results, we are confident that the proposed system can improve the speed and accuracy with which normal and diseased subjects are identified. Diseased subjects can be treated earlier which improves their probability of survival.

A comparison of the analysis of methods for feature extraction and classification by Wavelet transform in SSVEP BCIs

2021 ◽  
Author(s):  
Hoda Heidari ◽  
zahra einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Vector Machines ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods which includes 1) feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) and wavelet transform magnitude, 2) feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), 3) classification step applying k nearest neighbor (k-NN), support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down. The highest level of accuracy was obtained 91.39%.

scholarly journals Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4239
Author(s):  
Zhiyuan Wu ◽  
Hanying Zhang ◽  
Wentao Sun ◽  
Ning Lu ◽  
Meng Yan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Visual Observation ◽  
K Nearest Neighbor ◽  
Sampling Device ◽  
Airflow Distribution ◽  
The Government ◽  
The Cost

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

Sign in / Sign up

Export Citation Format

Share Document