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The paper describes the application of different wavelet analysis together with machine learning algorithm for the recognition of English alphabet from EEG signal. Decision making was executed in two stages. At first important features such as maximum, minimum, delta value, moment, kurtosis, skew, median, mean and standard deviation at different sub-bands are computed using the wavelet functions —...
Classification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog...
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. Still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist...
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