Andere Kunden interessierten sich auch für
![Automatic Spillway Gates Controlling. Real Time Monitoring Using Radio Frequency]( "Automatic Spillway Gates Controlling. Real Time Monitoring Using Radio Frequency")
Automatic Spillway Gates Controlling. Real Time Monitoring Using Radio Frequency14,99 €![Effects of Time-Varying Magnetic Fields in the Frequency Range 1 kHz to 100 kHz upon the Human Body : Numerical Studies and Stimulation Experiment]( "Effects of Time-Varying Magnetic Fields in the Frequency Range 1 kHz to 100 kHz upon the Human Body : Numerical Studies and Stimulation Experiment")
Effects of Time-Varying Magnetic Fields in the Frequency Range 1 kHz to 100 kHz upon the Human Body : Numerical Studies and Stimulation Experiment45,00 €![Development of New Hybrid Models for Prediction of Maximal Oxygen Uptake (VO2max) Using Machine Learning Methods Combined with Feature Selection Algorithms]( "Development of New Hybrid Models for Prediction of Maximal Oxygen Uptake (VO2max) Using Machine Learning Methods Combined with Feature Selection Algorithms")
Development of New Hybrid Models for Prediction of Maximal Oxygen Uptake (VO2max) Using Machine Learning Methods Combined with Feature Selection Algorithms47,95 €![EEG Signal Processing Techniques and Applications]( "EEG Signal Processing Techniques and Applications")
EEG Signal Processing Techniques and Applications120,99 €![High Frequency Circuit Design-Second Edition-with 90 Circuit Design Examples]( "High Frequency Circuit Design-Second Edition-with 90 Circuit Design Examples")
High Frequency Circuit Design-Second Edition-with 90 Circuit Design Examples79,99 €![Detection and Feature Extraction in Acoustic Sensor Signals]( "Detection and Feature Extraction in Acoustic Sensor Signals")
Detection and Feature Extraction in Acoustic Sensor Signals80,99 €![Feature Papers in Materials Simulation and Design]( "Feature Papers in Materials Simulation and Design")
Feature Papers in Materials Simulation and Design111,99 €
Document from the year 2017 in the subject Medicine - Biomedical Engineering, grade: 3.0, , language: English, abstract: Electroencephalograph (EEG) has been widely used for BCI applications due to its non-invasiveness, ease of implementation, and cost-efficiency. The collected EEG signal is non-stationary and has task-related information buried in the frequency and temporal domains. In this book, we focused on developing time-frequency decomposition methods for improving the feature extraction module in BCI systems. The obtained features are then optimized by identifying subject-specific reactive band and employing evolutionary algorithm-based methods for optimizing the obtained features, improving the BCI systems' performance. A signal model named as band-limited multiple Fourier linear combiner (BMFLC) is employed to model the non-stationarity in the EEG signal for feature extraction. The non-stationary amplitude oscillation is presented as adaptive weights in the model and estimated with various adaptive filters such as least-mean square (LMS), Kalman filter (KF), or Kalman Smoother (KS). The estimated coefficients serve as features for classification. Our results suggest that the BMFLC-LMS, BMFLC-KF, and BMFLC-KS are all sufficient in modeling EEG signal in the band with average estimation accuracies of 93%, 99%, and 98%, respectively. We modelled motion-induced EEG signal in frequency domain. We found that most subjects present a subject- specific reactive band during motion tasks. We then constructed features for a classifier that used only the frequency information in the subject-specific reactive band. As a result, the classification accuracy of the BCI system is improved compared to the system which uses the complete band information. Features obtained from multiple EEG channels need to be optimized to enhance the performance of the BCI systems. Essentially, two problems need to be resolved: 1) volume conduction; 2) dimensionality. The volume conduction can be mitigated if spatial filter is employed, and the dimension of the feature vector can be reduced if a feature selection procedure is adopted. An evolutionary algorithm (EA) based approach was developed to estimate spatial filter and reduce feature dimension simultaneously. We show that the BMFLC-KF combined with the evolutionary al- algorithm has the highest classification accuracy compared to other BMFLC-KF based approaches and is superior to the traditionally employed band-power methods.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.