Artifact Characterization, Detection and Removal from Neural Signals

Abstract

Artifact detection and removal are important preprocessing steps for neural recordings to decode the neural signals properly and currently an active research problem. In this thesis, for the first time, artifacts found in the in-vivo neural recordings are studied and consequently a systematic artifact characterization is presented. Subsequently, three different artifact removal methods are proposed (one for in-vivo neural signals in general application and two for EEG data for two different application purposes, i.e. seizure detection and brain-computer interface (BCI) experiments). In order to evaluate the proposed methods quantitatively in comparison with available state-of-the-art methods, different artifact types have been extracted from real recordings for simulating artifact templates and hence to build a synthesized neural database on which the methods are applied. In addition, the effects of artifact removal on the later-stage processing have also been evaluated resulting significant performance improvement which proofs the efficacy of the proposed methods.