Wavelet based head movement artifact removal from electrooculography signals

Electrooculography (EOG) signals acquire different types of eye movements, which can be employed for human-machine interfaces (HMI) and also for diagnostic purposes. In realistic circumstances, EOG signals tend to be contaminated with noise due to unconstrained head movements. This noise degrades the signal quality as well as increases the misclassification rate of eye movement detection. General filtering and preprocessing techniques are unable to remove this noise. This paper presents a novel approach of head-movement noise removal from EOG signals by employing a biorthogonal wavelet transform to extract the level-4 approximation coefficients, which are also exploited as features classified by k- nearest neighbor (kNN) classifier. This approach enhances the classification performance remarkably. Even when this wavelet based technique is applied as denoising technique and features to the prior arts, it improves the performance of those existing techniques too. Moreover, the proposed technique is suitable for real time applications.