Performance comparison of IST and multi scale principal component analysis in the EEG signal processing

The removal of Ocular Artifacts (OA) in Electroencephalogram (EEG) data is one of the key challenges in the analysis of brain recordings. Brain activity produces electroencephalogram signals, which consists of some of vital signs of neurological disorders such as epilepsy, tumor cerebrovascular lesions and the problems associated with the trauma. These signals can be acquired by placing the electrodes on the scalp at specified positions and exists in order of 1–5μv, whose frequency range is DC-64 Hz. Acquisition of these signals mainly suffers from different unwanted signals (artifacts or noise) resulting in less signal information for identification. In this paper, two algorithms are proposed namely, Multi Scale Principal Component Analysis (MSPCA) and Iterative Soft Thresholding (IST) using wavelets in removing the Ocular Artifacts (OA) present in the EEG signals. This paper discusses not only the performance comparison of two algorithms on statistical parameters of EEG signals such as Signal to Noise Ratio, (SNR), SNRI or Noise Figure (NF) and Absolute Average Error (AAE) but also estimated the run time of each algorithm i.e., computational time of each algorithm.