Study on Identification Algorithm of EEG Imaginary Movements

2012 ◽  
Vol 182-183 ◽  
pp. 1885-1889
Author(s):  
Jing Zhou ◽  
Li Jun Li ◽  
Ning Shan Li ◽  
Xiao Ming Wu ◽  
Rong Qian Yang
Keyword(s):  
Time Domain Analysis ◽  
Domain Analysis ◽  
Energy Difference ◽  
Frequency Domain Analysis ◽  
Support Vector ◽  
Identification Algorithm ◽  
Eeg Signals ◽  
Vector Method ◽  
Electroencephalogram Eeg ◽  
Bci System

Movement whether it is actual or imaginary can produce different electroencephalogram (EEG) signals. How to extract features of signals and accurately classify them is a key to brain-computer interface(BCI) system. In the paper, BCI competition data downloaded from BCI website are used as study object, through time-domain analysis and frequency-domain analysis, according to the attribute of event-related synchronization (ERS) and event-related desynchronization (ERD) during imagery movement, energy difference of lead C3 and C4 are selected as features and wavelet package is used to extract them. Probabilistic neural networks (PNN) is used as classification method. Compared with other two calssification methods such as support vector method (SVM) and liner classifier, the classification accuracy rate of PNN reaches to 89.2% steadily and is higher than them. It is proved that the method provided in the paper are effective for identifying imaginary movements.

scholarly journals Brain connectivity at different time-scales measured with EEG

2005 ◽  
Vol 360 (1457) ◽  
pp. 1015-1024 ◽  
Author(s):  
T Koenig ◽  
D Studer ◽  
D Hubl ◽  
L Melie ◽  
W.K Strik
Keyword(s):  
Frequency Domain ◽  
Brain Connectivity ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Brain Regions ◽  
Frequency Domain Analysis ◽  
Functional Magnetic Resonance ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
The Brain

We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.

Partial Discharge Identification Using Multiple Features Fusion Based on Evidence Theory and Support Vector Machine

2013 ◽  
Vol 397-400 ◽  
pp. 2111-2119
Author(s):  
Yuan Xu
Keyword(s):  
Support Vector Machine ◽  
Probability Model ◽  
Partial Discharge ◽  
Evidence Theory ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Recognition System ◽  
Frequency Domain Analysis ◽  
Support Vector ◽  
Time Resolved

The time resolved partial discharge (TRPD) signals collected by Ultra High Frequency (UHF) sensors of transformer contain important informations of the properties of faults. Wealth of information can be utilized for fault identification by different signal analyzing techniques, such as time domain analysis, frequency domain analysis and the wavelet analysis. An integrated characteristic information fusion system based on the Platt posterior probability model and support vector machine (SVM) one-against-one method is proposed. From the recognition result of the four typical insulation defects, the recognition system shows strong advantages in accuracy and reliability.

scholarly journals Analysis of Three-Phase Wye-Delta Connected LLC

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3606
Author(s):  
Jing-Yuan Lin ◽  
Chuan-Ting Chen ◽  
Kuan-Hung Chen ◽  
Yi-Feng Lin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Operation Time ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Complete Analysis ◽  
Plane Analysis ◽  
Analysis Methods ◽  
Three Phase

Three-phase wye–delta LLC topology is suitable for voltage step down and high output current, and has been used in the industry for some time, e.g., for server power and EV charger. However, no comprehensive circuit analysis has been performed for three-phase wye–delta LLC. This paper provides complete analysis methods for three-phase wye–delta LLC. The analysis methods include circuit operation, time domain analysis, frequency domain analysis, and state–plane analysis. Circuit operation helps determine the circuit composition and operation sequence. Time domain analysis helps understand the detail operation, equivalent circuit model, and circuit equation. Frequency domain analysis helps obtain the curve of the transfer function and assists in circuit design. State–plane analysis is used for optimal trajectory control (OTC). These analyses not only can calculate the voltage/current stress, but can also help design three-phase wye-delta connected LLC and provide the OTC control reference. In addition, this paper uses PSIM simulation to verify the correctness of analysis. At the end, a 5-kW three-phase wye–delta LLC prototype is realized. The specification of the prototype is a DC input voltage of 380 V and output voltage/current of 48 V/105 A. The peak efficiency is 96.57%.

scholarly journals A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 115-121
Author(s):  
Asrul Adam ◽  
Ammar Faiz Zainal Abidin ◽  
Zulkifli Md Yusof ◽  
Norrima Mokhtar ◽  
Mohd Ibrahim Shapiai
Keyword(s):  
Feature Selection ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Peak Detection ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Eeg Signals ◽  
Selection Algorithms

In this paper, the developments in the field of EEG signals peaks detection and classification methods based on time-domain analysis have been discussed. The use of peak classification algorithm has end up the most significant approach in several applications. Generally, the peaks detection and classification algorithm is a first step in detecting any event-related for the variation of signals. A review based on the variety of peak models on their respective classification methods and applications have been investigated. In addition, this paper also discusses on the existing feature selection algorithms in the field of peaks classification.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Pulse Wave Analysis of Traditional Chinese Medicine Based on Hemodynamics Principles**

2011 ◽  
pp. 194-203
Author(s):  
Rui Guo ◽  
Yiqin Wang ◽  
Haixia Yan ◽  
Fufeng Li ◽  
Jianjun Yan ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Significance ◽  
Pulse Wave ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Pulse Velocity ◽  
Reflection Coefficients ◽  
Feature Parameters

From the perspective of hemodynamics principles, the pressure pulse wave marked in the radial artery is the comprehensive result of pulse wave propagation and reflection in the arterial conduit. The most common pulse charts (also called pulse wave) obtained by Traditional Chinese Medicine (TCM) pulse-taking technique, if quantified and standardized, may become a universal and valuable diagnostic tool. The methods of feature extraction of TCM pulse charts currently involve time-domain analysis, frequency-domain analysis and time-frequency joint analysis. The feature parameters extracted by these methods have no definite clinical significance. Therefore, these feature parameters cannot essentially differentiate different types of TCM pulse. In this chapter, the harmonic analysis method was applied to analyze the common TCM pulse charts (plain pulse, wiry pulse, slippery pulse). Velocity and reflectivity coefficients of pulse were calculated. We found that wave velocities and reflection coefficients of different TCM pulse have different distributions. Furthermore, we studied the clinical significance of velocities and reflection coefficients. The result suggests that wave velocity and reflection coefficient are the feature parameters of TCM pulse with physiological and pathological significance, which can be used to interpret formation of Chinese medicine pulse. Our study reveals the mechanism of TCM pulse formation and promotes non-invasive TCM pulse diagnostic method.

Modelling of Electrohydraulic Systems with Transmission Lines using Modal Approximations

1988 ◽  
Vol 202 (3) ◽  
pp. 153-163 ◽  
Author(s):  
J Watton
Keyword(s):  
Transmission Lines ◽  
Method Of Characteristics ◽  
Digital Simulation ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Specific Form ◽  
Lumped Parameter ◽  
Good Comparison ◽  
Distributed Friction

The method of modal approximation to the distributed friction transmission line functions via frequency-domain analysis is briefly discussed. A specific form is then derived which allows time-domain analysis to be easily pursued using a digital simulation package approach. The method is applied to a highly non-linear servo-valve controlled motor system and a good comparison between experiment and theory is shown. A comparison is also made with previous work using the method of characteristics, and natural frequency predictions are also compared with some common lumped parameter approximations.

scholarly journals A New Conceptual Design and Dynamic Analysis of a Spar-Type Offshore Wind Turbine Combined with a Moonpool

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3737 ◽  
Author(s):  
Thanh Dam Pham ◽  
Hyunkyoung Shin
Keyword(s):  
Wind Turbine ◽  
Bending Moment ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Operational Conditions ◽  
Floating Offshore Wind Turbines

Floating offshore wind turbines promise to provide an abundant source of energy. Currently, much attention is being paid to the efficient performance and the economics of floating wind systems. This paper aims to develop a spar-type platform to support a 5-MW reference wind turbine at a water depth of 150 m. The spar-type platform includes a moonpool at the center. The design optimization process is composed of three steps; the first step uses a spreadsheet to calculate the platform dimensions; the second step is a frequency domain analysis of the responses in wave conditions; and the final step is a fully coupled simulation time domain analysis to obtain the dynamic responses in combined wind, wave, and current conditions. By having a water column inside the open moonpool, the system’s dynamic responses to horizontal and rotating motions are significantly reduced. Reduction of these motions leads to a reduction in the nacelle acceleration and tower base bending moment. On the basic of optimization processes, a spar-type platform combined with a moonpool is suggested, which has good performance in both operational conditions and extreme conditions.

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