Influential stimuli characteristics on SNR in SSVEP-based interfaces

Thesis report: researching different aspects that influence the SNR in SSVEP-based interfaces

Master thesis (2023)

Authors

S.T. van Vliet Mechanical Engineering

Contributors

Y.B. Eisma Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (mentor)
J.C.F. de Winter Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (graduation committee member)
Aart J. Nederveen Universiteit van Amsterdam (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2023 Sjoerd van Vliet
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Published Date

17-02-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

Different paradigms can be used to evoke brainwaves. These brainwaves can be interpreted as commands that can be used to control different applications. These frameworks that interpret the brainwaves are called brain-computer interfaces. Steady-state visually evoked potentials is one of these paradigms that uses external stimuli flickering at fixed frequencies to evoke brainwaves. This paradigm is fast to issue, is reliable, and needs no training time of the user. However, to be able to distinguish multiple commands it is important to distinguish between multiple commands reliably. The fundamental metric that determines the signal quality is the signal-to-noise ratio. The signal-to-noise ratio is measured in decibels and is the ratio in power between the signal that is evoked around the stimulus frequency and the power of some baseline, referred to as noise.
To extract the power the discrete Fourier transform is calculated from the measured brainwaves. The brainwaves are measured in millivolts over time. These brainwaves can be recorded using implants in the head named intracortical or external, such as electroencephalography. External methods such as electroencephalography are much safer for the user.

Important to recognize is that one of the contributing factors to signal-to-noise ratio are the characteristics of the external stimuli displayed. Research is still trying to figure out the exact relationship between stimuli characteristics and signal-to-noise ratio. However, the experiments of previous research lack the context of the gaze of the subjects to explain the electroencephalography recordings. In this research, this is attempted to be solved using eye tracking. Furthermore, there is a research gap in the
the scientific field surrounding signal-to-noise ratio and stimuli characteristics as the effect of surrounding
stimuli on the measured signal-to-noise ratio of the target stimulus has never been investigated.

This research attempted to solve these problems by performing 2 experiments with 6 participants. One experiment shows a single stimulus across various shapes (triangles, squares, and circles), colors (red, green, and white), frequencies (9, 13, 19, and 25Hz), and sizes (10.000, 20.000, 30.000 pixels). The other experiment simulates the natural environment of the external stimuli across the same frequencies, colors, and shapes. The natural environment of a single stimulus is actually surrounding stimuli at different frequencies, as applications often require the ability to distinguish between multiple different commands. Thus, to state the main research question: "What is the relationship between the stimuli characteristics and the measured SNR?".
This question is answered by dissecting the effect that color, shape, size, frequency, and surrounding stimuli have on the signal-to-noise ratio.