Comprehensive Study On Removal Of Artifacts From Single-Channel EEG

Electroencephalogram (EEG) signals have a long history of use as a noninvasive brain functional measurement. As people gradually understanding of biological electrical signal and the development of related technologies, more and more application fields are related to people’s life. One of the main application fields are based on EEG signals subject of clinical study, the researchers are trying to explore the brain electrical signals between the brain and to explore the brain mechanism, so that people developed a convenient, smart machines for the benefit of humanity. In fact, collected EEG signals from the instrument will be contaminated by various artifact of EEG signals. So, the identification and removal of the undesirable artifacts are of prime necessity to make easier data interpretation and representation and recover the signal that matches the brain which functions perfectly. Although a number of methods have been proposed in recent years this area, few studies have discussed based on the forecast of EEG signals to remove artifacts.In eeg preprocessing has three main noise signal:baseline drift and noise power frequency electric artifact and the eye need to detect and remove. Based on the three brain electrical noise signal AR model method is used to automatically detect the noise, and put forward an adaptive modeling technique that combines Discrete Wavelet Transformation (DWT) to predict contaminated EEG signals for removal of ocular artifacts (OAs) from EEG records is proposed as an effective a data processing tool for Interventions in Mental Illness Based on Bio-feedback. The proposed method is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. Using simulated and measured data the accuracy of the proposed model is compared to the accuracy of other pre-existing methods based on Wavelet Packet Transform (WPT) and independent component analysis (ICA) using DWT and adaptive noise cancellation (ANC) for Portable applications. The results show that the our new model not only demonstrates an improved performance with respect to the recovery of true EEG signals, achieves improved computational speed, and demonstrates better tracking performance. In addition, in order to make the new algorithm can be further applied to the actual project, we use the classical paradigm of psychological experiments (N-Back), use the EEG signals were collected under this paradigm, to measure the performance of the pretreatment system.The experimental results show that based on adaptive predictor algorithm not only can effectively detect eye electric artifact, and can effectively eliminate eye electrical interference, improve the recognition rate of P300.