Filter Out High Frequency Noise in EEG Data Using The Method of Maximum Entropy

TL;DR

This paper introduces a maximum entropy-based filtering method to effectively reduce high-frequency noise in EEG data, especially after second derivative calculations, outperforming existing filters.

Contribution

The paper presents a novel ME-based filter design approach tailored for EEG data noise reduction, including a systematic method for filter selection within a generated family.

Findings

ME filters outperform traditional EEG filters in noise reduction

The approach effectively minimizes noise variance after filtering

Frequency analysis confirms superior performance of ME filters

Abstract

We propose a maximum entropy (ME) based approach to smooth noise not only in data but also to noise amplified by second order derivative calculation of the data especially for electroencephalography (EEG) studies. The approach includes two steps, applying method of ME to generate a family of filters and minimizing noise variance after applying these filters on data selects the preferred one within the family. We examine performance of the ME filter through frequency and noise variance analysis and compare it with other well known filters developed in the EEG studies. The results show the ME filters to outperform others. Although we only demonstrate a filter design especially for second order derivative of EEG data, these studies still shed an informatic approach of systematically designing a filter for specific purposes.