Abstract

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

Highlights

  • The investigation of brain waves with the implementation of the EEG data is very useful, as it can help understanding human mental condition such as stress, burnout exhaustion, or mental fatigue [1,2,3,4,5]

  • One of the key factors leading the authors to this research was is the great popularity of the Emotiv EPOC Flex headset among young researchers, which tend to believe that the acquired signal is reliable

  • To sum it all up, the thorough literature study did not find any papers regarding comparison of the smoothing filters the way this has been done in this paper

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Summary

Introduction

The investigation of brain waves with the implementation of the EEG (electroencephalography) data is very useful, as it can help understanding human mental condition such as stress, burnout exhaustion, or mental fatigue [1,2,3,4,5]. It can provide information regarding the health condition of the tested subjects without the need of surgical intervention [6]. The device uses 5-th order Sinc filter—0.5–45 Hz and Notch filters (at 50 and 60 Hz) for the power disturbances removal [7]

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