Effects of Digital Filtering on the Classification Performance of Steady-State Visual Evoked Potential Based Brain-Computer Interfaces

Volkan Çeti̇n,Serhat Özekes,Huseyin Selcuk Varol

doi:10.17694/bajece.654288

Abstract

The electrical activity that occurs during the communication of neurons is recorded by a method called electroencephalography. Brain computer interfaces utilize various electrophysiological sources obtained from different regions of the brain. The electrophysiological source used in this study is the electrical activity seen in the occipital lobes as a result of visual stimuli that flicker at certain frequencies, and is called steady-state visual evoked potential. The main goal in this work is not to try to improve the classification performance but to investigate the effects of different digital filtering algorithms on classification performance. The effects of the high pass and low pass filtering on the classification performance in steady-state visual evoked potential based brain computer interfaces are investigated. As a result of this study, no significant change in the classification performances of designs with only high pass filtering, and high and low pass filtering, has been observed. In addition, it has been observed that only the designs include a high-pass filter implementation give better classification performance in many cases. Consequently, it is concluded that low-pass filtering in steady-state visual evoked potential based brain-computer interfaces does not provide the desired contribution to classification performance.

Highlights

EVOKED ELECTRICAL signals, caused by a visual stimulus are called visual evoked potentials and are recorded from the occipital and parietal lobes of the brain
They applied a binary classification on EEG acquired from O1 and O2 electrodes, and used two stimuli with frequencies of 6 Hz and 25 Hz. They applied a spectral power density (SPD)-based classification, and used the arithmetic mean of the SPD values obtained from O1 and O2 electrodes as features
On the http://dergipark.gov.tr/bajece other hand, it is observed that the digital LPF does nothaveasignificanteffectontheclassificationperformanceandin mostcasesithasa negative effect. This can be explained by the fact that the electronic LPF in the neuro-headset is capable of providing adequate classification performance

Summary

Introduction

EVOKED ELECTRICAL signals, caused by a visual stimulus are called visual evoked potentials and are recorded from the occipital and parietal lobes of the brain. Prueckl and Guger developed a four-task classification of forward, backward, right and left with a SSVEP-based BCI using four stimuli at Hz, Hz, Hz and Hz frequencies [1]. They used the first and second harmonics obtained from the frequency spectrum of the SSVEP. They acquired the EEG from O1, O2, Oz, PO3, PO4, PO7, PO8 and POz electrodes, and perform a SPD-based classification. Volosyak developed a SSVEP-based spelling system with a classification of six tasks Based on the five different SSVEP stimuli at frequencies 6.67 Hz, 7.5 Hz, 8.57 Hz, 10 Hz and 12

Methods

Results

Conclusion