Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals HIL evaluation of control unit in grid-tied coverters

2016 ◽  
Vol 20 (suppl. 2) ◽  
pp. 393-406 ◽  
Author(s):  
Vlado Porobic ◽  
Evgenije Adzic ◽  
Milan Rapaic
Keyword(s):  
Power Electronics ◽  
High Performance ◽  
Control Unit ◽  
Active Filters ◽  
Design Tool ◽  
Operating Conditions ◽  
Real Time Control ◽  
Time Control ◽  
Power Electronics Converters ◽  
Grid Connected Converters

Hardware-in-the-Loop (HIL) emulation is poised to become unsurpassed design tool for development, testing, and optimization of real-time control algorithms for grid connected power electronics converters for distributed generation, active filters and smart grid applications. It is strongly important to examine and test how grid connected converters perform under different operating conditions including grid disturbances and faults. In that sense, converter?s controller is a key component responsible for ensuring safe and high-performance operation. This paper demonstrates an example how ultra-low latency and high fidelity HIL emulator is used to easily, rapidly and exhaustively test and validate standard control strategy for grid connected power electronics converters, without need for expensive hardware prototyping and laboratory test equipment.

Flow control with active dimples

2007 ◽  
Vol 111 (1125) ◽  
pp. 705-714 ◽  
Author(s):  
S. Dearing ◽  
S. Lambert ◽  
J. Morrison
Keyword(s):  
Pressure Sensors ◽  
Fast Response ◽  
Vortex Generators ◽  
Control Surface ◽  
Real Time Control ◽  
Capacitance Change ◽  
Time Control ◽  
Design And Manufacture ◽  
Smart Skin

Abstract The long-term goal is to design and manufacture optimal ‘on-demand’ vortex generators, ‘dimples’ that can produce vortices of prescribed strength and duration for the real-time control of aerodynamic flows that are either undergoing transition or are fully turbulent, attached or separating. Electro-active polymers (EAP) are ideal for a dimple control surface, offering high strain rate, fast response, and high electromechanical efficiency. EAP can also be used as the basis of a resistanc – or capacitance – change pressure sensor, development of which has just begun. In terms of manufacture, inkjet printing of EAP also offers a paradigm shift such that a monolithic control surface is a very real possibility. Important features for integration into a control system are robustness and a predictable, repeatable motion. With these objectives in mind, the suitability of EAP-based actuators is assessed both mechanically and aerodynamically. The ultimate goal is to integrate these devices, along with shear-stress and pressure sensors and distributed control, also under development, into a flexible ‘smart skin’ which could be incorporated into an airframe structure. The response of a laminar boundary layer to forcing is investiagted using mechanical dimples.

Hardware Design of Diesel Generator Sets Digital Control System Based on DSP

2008 ◽  
Vol 44-46 ◽  
pp. 563-568
Author(s):  
Cun Lu Dang ◽  
L. Chai ◽  
Xiao Ying Zhang
Keyword(s):  
Control System ◽  
Digital Control ◽  
High Performance ◽  
Hardware Design ◽  
Digital Control System ◽  
Diesel Generator ◽  
Real Time Control ◽  
Time Control ◽  
And Performance ◽  
Generator Sets

The diesel generator sets digital control system based on DSP is studied in this paper. The principles of voltage and speed control are analyzed. The main DSP chip is 16-bit fixed-point TMS320LF2407A. The hardware design, the former channels, the after channels and the CAN controller are accomplished in this paper. The simulation results indicate the controller has high-performance processing capabilities and it can meet the need of Generator Sets' real-time control and performance requirements.

Real Time Control of Thermal Distortion During Pulsed Laser Welding of Optical Packages

1996 ◽  
Vol 118 (3) ◽  
pp. 445-448
Author(s):  
Musa Jouaneh ◽  
Sabbir Rangwala
Keyword(s):  
Laser Welding ◽  
Pulsed Laser ◽  
Critical Time ◽  
Fast Response ◽  
Piezo Actuator ◽  
Thermal Distortion ◽  
Real Time Control ◽  
Pulsed Laser Welding ◽  
Time Control ◽  
Time Period

This paper investigates a method to compensate for thermal distortions which occur during pulsed laser welding of optical packages. A control strategy, based on the use of a fast response piezo-actuator to maintain relative alignment between the fiber and the laser in the critical time period after application of the laser pulse and before cooling of the weld nugget is described.

scholarly journals Disturbance Compensation in Fuzzy Logic Control of Level in Carbonisation Column for Soda Production

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Fuzzy Logic ◽  
High Performance ◽  
Fuzzy Logic Controller ◽  
General Purpose ◽  
Rule Base ◽  
Disturbance Compensation ◽  
Real Time Control ◽  
System Error ◽  
Model Free ◽  
Time Control

The intelligent approaches emerge as leading techniques in providing of stable and high performance control of industrial plants with nonlinearity, model uncertainty, variables coupling and disturbances. In the present research a novel approach for the design of a nonlinear model-free fuzzy logic controller (FLC) with two inputs – the system error and the main measurable disturbance and a rule base for disturbance compensation is suggested. It is based on off-line parameter optimisation via genetic algorithms. The approach is applied for the development of a FLC for the control of the level of ammonia brine solution in a carbonisation column with compensation of the changes in the inflow pressure. The control algorithm is implemented in a general purpose industrial programmable logic controller in ”Solvay Sodi” SA – Devnya, Bulgaria. The FLC system with disturbance compensation outperforms in an increased dynamic accuracy the FLC with the system error as a single input even when linear feedforward disturbance compensation is added. The performance of all systems is assessed from the real time control and the simulations based on a derived TSK plant model.

scholarly journals Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

2021 ◽  
Vol 12 (1) ◽  
pp. 168
Author(s):  
Rihards Novickis ◽  
Aleksandrs Levinskis ◽  
Vitalijs Fescenko ◽  
Roberts Kadikis ◽  
Kaspars Ozols ◽  
...  
Keyword(s):  
High Performance ◽  
Substantial Reduction ◽  
Road Accidents ◽  
Passenger Vehicle ◽  
Real Time Control ◽  
Automated Driving ◽  
Automotive Safety ◽  
Time Control ◽  
Perception System ◽  
Fusion Approach

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.

Deep Learning Based Tobacco Products Classification (Preprint)

2019 ◽  
Author(s):  
Murat Taşkıran ◽  
Sibel Çimen Yetiş
Keyword(s):  
Young People ◽  
Feature Vector ◽  
Weighted Average ◽  
Classification Performance ◽  
Accuracy Rate ◽  
Tobacco Products ◽  
Internet Environment ◽  
Performance Rate ◽  
Fully Connected

BACKGROUND Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. OBJECTIVE In this study, it is aimed to detect tobacco and tobacco products with various Convolutional Neural Networks (CNNs) and to limit the access of young users to these detected tobacco products over the Internet. METHODS A total of 1607 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for detect tobacco products. RESULTS MobileNet gave the highest results 99.1% as weighted average. The feature vector of the input images are extracted with CNNs and classified with the latest fully connected layer. CONCLUSIONS The classification of the tobacco products of 4 different classes was studied by using the networks and the classification performance rate was obtained as 100% for 322 test images via MobileNet. In this way, the content that is encouraging for children can be censored or filtered with a high accuracy rate and a secure Internet environment can be provided.

scholarly journals Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal

2021 ◽  
Vol 29 ◽  
pp. 519-529
Author(s):  
Sang-Hong Lee
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
High Performance ◽  
Fuzzy Membership ◽  
Eeg Signal ◽  
Performance Result ◽  
The Mean ◽  
New Feature ◽  
Background Feature

BACKGROUND: Feature selection is a technology that improves the performance result by eliminating overlapping or unrelated features. OBJECTIVE: To improve the performance result, this study proposes a new feature selection that uses the distance between the centers. METHODS: This study uses the distance between the centers of gravity (DBCG) of the bounded sum of the weighted fuzzy memberships (BSWFMs) supported by a neural network with weighted fuzzy membership (NEWFM). RESULTS: Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. The NEWFM used 22 minimum features as inputs to obtain a sensitivity, accuracy, and specificity of 99.3%, 99.5%, and 99.7%, respectively. CONCLUSIONS: In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution.

Sign in / Sign up

Export Citation Format

Share Document