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Mean Threshold and Arnn Algorithms for Classification of Eeg Commands



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This book introduces two Autoregressive Neural Network (ARNN) and mean threshold methods for recognizing eye commands for control of an electrical wheelchair using Electroencephalogram (EEG) technology. Eye movements such as "eyes open", "eyes blink", "glancing left" and "glancing right" . A Hamming low pass filter was applied to remove artifacts of eye signals for extracting the frequency ranges. An AR model was employed to produce coefficients, containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was applied for classifying eye movements. In comparison of two recognition methods, the purpose was to find the better one for applying in the electrical wheelchair.






This book introduces two Autoregressive Neural Network (ARNN) and mean threshold methods for recognizing eye commands for control of an electrical wheelchair using Electroencephalogram (EEG) technology. Eye movements such as "eyes open", "eyes blink", "glancing left" and "glancing right" . A Hamming low pass filter was applied to remove artifacts of eye signals for extracting the frequency ranges. An AR model was employed to produce coefficients, containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was applied for classifying eye movements. In comparison of two recognition methods, the purpose was to find the better one for applying in the electrical wheelchair.


provides a viable alternative to machine learning algorithms for BCIs. In this study a braincomputer interface BCI framework for hybrid functional nearinfrared spectroscopy fNIRS and electroencephalography EEG for lockedin syndrome LIS patients is investigated. Ensemble classifiers with the proposed overlapped partitioning were evaluated on our original P300based BCI data set data set A and BCI competition III data set II data set B as shown in Figure 1.The primary objective is to clarify how the overlapped partitioning for ensemble classifiers influences the classification accuracy. Brain tasks channel selection methods and feature extraction and classification algorithms available in the literature are reviewed. Currently the hybrid BCI is defined as the combination of either more than two BCI types or two brain signals with a single BCI type 7 8. There are various key elements in the BCI closedloop one being the classification algorithms a.k.a classifiers used to recognize the users EEG patterns based on EEG features.


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thesis Dept. Nguyen Thanh . Buy Mean Threshold and Arnn Algorithms for Classification of Eeg Commands by Nguyen Thanh Hai online on Amazon.ae at best prices. The and waves of the EEG signal are used to extract the features that represent the evoked activity in each group of frames using the PeakOverThreshold POT technique. Since ICA is becoming increasingly popular for EEG research efforts have been made to identify the best algorithms and prerequisites to obtain a good decomposition of the data. Alternating between state mean and covariance prediction is can assume diagonal. This can offer a clue in diagnosing various neurological conditions. Mean Threshold and ARNN Algorithms for Classification of EEG Commands 9783659572142 This book introduces two Autoregressive Neural Network .


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