Abstract
This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.
Highlights
Distractions can be loosely defined as events that take away the attention from what you are supposed to be doing
We present several results in relation to the dependent variables, such as repeated measures ANOVA, to determine the effect that lab condition (LC), music at 30% (M30), music at 60% (M60), music at 90% (M90), ambient noise (AN), passive talking (PT), and active listening (AL) distractions have on the online performance, offline statistics, and user preference
Descriptive analysis shows that the highest amplitude was for PT and the lowest amplitude was for AL
Summary
Distractions can be loosely defined as events that take away the attention from what you are supposed to be doing. The work presented here is part of a larger electroencephalography (EEG)-based project and in continuation of our latest papers [1,2], with the goal of building an extendible hierarchical taxonomy aimed at categorizing distractions and the effects thereof. Many BCI applications and experiments were and are still being performed in laboratory settings with unrealistic conditions, where the subject sits in a sound-attenuated room without any distractions [4,5]. A number of research papers, such as [6,7], focus on real-world contexts, they were either using medical-grade equipment [8] and/or focusing on auditory event-related potentials (ERPs) [9]
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