An efficient soft-computing technique for extraction of EEG signal from tainted EEG signal

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.

Figure optionsDownload as PowerPoint slideHighlights
► The recorded EEG signals that originate from non-cerebral origin are termed as artifacts. EEG data is almost tainted by such artifacts and that will make interpretation of EEGs clinically difficult.
► Adaptive noise cancellation technique based on ANFIS has been used to remove the artifact signal from the EEG signal.
► ANFIS is used for estimation and removal of the component of EOG, combined EOG and EMG signal present in the EEG signal.
► The results of the proposed technique are compared with the results of neural network based on ADALINE and adaptive filtering which makes use RLS of algorithm through wavelet transform.
► ANFIS outperforms the other two techniques even if the complexity of the signal is very high.