Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique

The electroencephalogram (EEG) is susceptible to various large signal contaminations or artifacts. Ocular artifacts act as major source of noise, making it difficult to distinguish normal brain activities from the abnormal ones. In this letter, an efficient technique that combines two popular adaptive filtering techniques, namely adaptive noise cancellation and adaptive signal enhancement, in a single recurrent neural network is proposed for the adaptive removal of ocular artifacts from EEG. A real time recurrent learning algorithm is employed for training the proposed neural network which converges faster to a lower mean squared error. This technique is suitable for real-time processing.