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.

In this paper, we ensue on the development of a taxonomy aimed at categorizing distractions in the P300b domain. Explicitly, we investigate the effect that auditory distractions, distinctively that of ambient noise (AN), passive talking (PT), and active listening (AL) have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. This work is part of a larger EEG based project and is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with eight healthy subjects; while using a non-invasive Brain-Computer Interface based on low fidelity electroencephalographic (EEG) equipment. Our results show that the accuracy was best for the Lab (LC) at 100%, followed by AN at 92.5%, PT at 90% and last AL at 87.5%, which results were in identical order to the subjects’ preferences. In addition, the amplitude and latency did not show any statistical significance in all settings. This paper provides additional results that impart insight into the practicability of the aforementioned P300 speller methodology and low-cost equipment to be used in real-world applications.

In this paper, we investigate the effect, in terms of amplitude and latency, of the P300 component in a separate active and passive task response condition. This work is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with five healthy subjects; while using a noninvasive Brain-Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. Our results suggest that an active task yielded a larger P300 peak amplitude while there was no discriminable difference in the peak latency. The signal was also morphological consistent in both scenarios, even though they did not yield identical P300 components. This groundwork yields imperative data for future work where we plan to introduce several distractions, including communication with the user while performing the P300 speller paradigm.

This paper introduces a hierarchical taxonomy for different categories of distractions that are commonly encountered in a real-life environment. In this work, we implicitly focus on auditory distractions with contrasting intensity levels, explicitly that of no music (M0), music at 30% (M30), music at 60% (M60), and music at 90% (M90) i.e. our independent variables, and the effect that these distractions have on our dependent variables i.e. amplitude, latency, accuracy, user preference and signal morphology while using a visual P300 Speller. The research method for this study includes the use of a visual P300 speller based on the oddball paradigm in conjunction with the xDAWN algorithm. This work employed the use of N = 10 healthy subjects while utilizing low-cost electroencephalographic (EEG) equipment. This study forms part of a series of studies based on EEG, focused on ensuing in the development of the aforementioned taxonomy. Our results show that for the accuracy dependent variable, the M0 at 100% was preeminent, trailed surprisingly by M90 at 98%, and equally by M60 and M30 at 96%. The subjects’ preference prodigiously shows that the preferred condition was M0 as originally expected, followed by M90, M60, and M30, which are inadvertently in the same order to the accuracy dependent variable. Statistical analysis between all independent variables accepts our null hypothesis for the amplitude and latency. Other statistical results such as the comparison of each independent variable with M0 is included in this paper. The results from this work should give an overview of the viability and feasibility of the aforementioned P300 speller methodology and equipment to be utilized in the real-world environment.

In this paper we investigate the viability, practicability and efficacy of eliciting P300 responses based on the P300 speller BCI paradigm (oddball) and the xDAWN algorithm, with five healthy subjects; while using a non-invasive Brain Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. The experiments were performed in three distinctive environments: lab conditions, mild and controlled user distractions, and real world environment (realistic sound and visual distractions present). Our main contribution is the assessment of the ways and extents to which different degrees of user distraction affect the detection success achievable using low fidelity equipment. Our results demonstrate the applicability of using off-the-shelf equipment as a means to successfully and effectively detect P300 responses, with different degrees of success across the three distinctive types of environment.

Brain Computer Interface (BCI) on the basis of Electroencephalography (EEG) has gained prominence over the past decade, especially with the proliferation of cheap EEG devices including user-made equipment. The main shortcoming of EEG, particularly with this type of equipment, is that it is frequently contaminated by various artifacts. Moreover a number of researchers and end users are currently using off-the-shelf equipment as a “black box” approach without any qualitative testing. This exposed an evident necessity to validate the equipment’s suitability. In this paper we provide vital groundwork by identifying and categorizing artifacts using our specific low fidelity equipment. This work forms part of a wider project where we assess the viability of this equipment, primarily in the artifacts domain, as a precursor for further studies. Our promising results show that we were able to effectively identify and categorize the most commonly encountered artifacts with the aforementioned equipment.

Abstract In this paper we investigate the viability, practicability and efficacy of eliciting P300 responses based on the P300 speller BCI paradigm (oddball) and the xDAWN algorithm using five healthy subjects; while using a non-invasive Brain Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. In the past decade there was a proliferation of cheap EEG equipment, including user-made equipment, which exposed an evident necessity to validate the equipment’s suitability. Moreover a number of researchers and end users are currently using off-the-shelf equipment as a “black box” approach without any qualitative testing. Part of our contribution will be to create awareness of what type of hardware components are being utilized in our low fidelity equipment, vis-à-vis the results achieved. Our main contribution is to assess the functionality and reliability of our low cost equipment in its ability to detect the P300 component in a consistent, reliable and effective manner as a basis for future studies. This work forms part of a wider project where we plan to introduce a number of distractions and assessing the ways and extents to which different degrees of distractions affect the detection success achievable of the P300 component while using our low cost equipment. Our results demonstrate the applicability of using this off-the-shelf equipment as a means to successfully and effectively detect P300 responses.

The use of Electroencephalography (EEG) signals in the field of Brain Computer Interface (BCI) has gained prominence over the past decade, with the availability of diverse applications especially in the clinical sector. The major downside is that the current equipment being used at medical level is specialized, complex and very expensive. Our research goals are to further increase accessibility to this technology by providing a unique approach in data analysis techniques, which in return will allow the usage of cheaper and simpler EEG hardware devices targeted for end users. We use non-invasive BCIs designed on EEG, mainly due to its fine temporal resolution, portability and ease of use. The main shortcoming of EEG is that it is frequently contaminated by various artefacts. In this paper we provide vital groundwork by identifying and categorizing artefacts using low fidelity equipment. This work forms part of a wider project in which we attempt to use those artefacts constructively, when others try to filter them out. The main contribution is to create awareness of the extent to which artefacts can be encountered, identified and categorized using off-the shelf equipment. Our results illustrate that we are able to adequately identify and categorize the most commonly encountered artefacts in a non-clinical environment, using low fidelity equipment.