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Using ANC and Wavelet Based Technique for De-Noising the Ocular Artifact and Detection of Epileptic in EEG signal

M.Thenmozhi, Ms.K.Subhashini

Introduced a new model using DWT and ANC techniques to remove the OAs in contaminated EEG signals. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. The model is also applied and evaluated against data recorded within the EUFP 7 Project—Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.After that, artifacts removal signals are received to identify the brain condition and then extract the feature.Features like mean, median ,and wavelet based features are extracted from the signal.The extracted features are classified using SVM classifier. System matches the signals with the true label to decide the condition is occurred in brain