Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
MATLAB Supplement to Fuzzy and Neural Approaches in Engineering,
MATLAB Supplement to Fuzzy and Neural Approaches in Engineering,
Robust adaptive techniques for minimization of EOG artefacts from EEG signals
Signal Processing - Signal processing in UWB communications
An automatic analysis method for detecting and eliminating ECG artifacts in EEG
Computers in Biology and Medicine
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
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Electroencephalography (EEG) is the recording of electrical activity of neurons within the brain and is used for the evaluation of brain disorders. But, EEG signals are contaminated with various artifacts which make interpretation of EEGs clinically difficult. In this research paper, we use a soft-computing technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) for the removal of EOG artifact, combined EOG and EMG artifact. Improvement in the output signal to noise ratio and minimum mean square error are used as the performance measures. The outputs of the proposed technique are compared with the outputs of techniques such as neural network, based on ADALINE (Adaptive Linear Neuron) and adaptive filtering method, which makes use of RLS (Recursive Least Squares) algorithm through wavelet transform (RLS-Wavelet). The obtained results show that the proposed method could significantly detect and suppress the artifacts.