The Effect That an Auditory Distraction with Differing Levels of

Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity

Equipment: Alongside the Development of a Taxonomy

Patrick Schemrbi

, Mariusz Pelc

and Jixin Ma

Department of Computer and Information Systems, University of Greenwich, Greenwich, London, U.K.

Keywords: Brain-Computer Interface (BCI), Electroencephalography (EEG), Event-Related Potential (ERP), P300

Speller, Distractions, Taxonomy.

Abstract: In this paper, we investigate the effect that an auditory distraction with differing levels of intensity has 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 based on the P300 speller BCI (oddball) paradigm and the xDAWN

algorithm, with ten healthy subjects; while using a non-invasive Brain-Computer Interface (BCI) based on

low fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the

no music (M0), followed by music at 90% (M90), music at 60% (M60) and last music at 30% (M30), which

results were in identical order to the subjects' preferences. In addition, the amplitude did not show any

statistical significance in all scenarios while the latency exhibited a minor statistical difference. This work is

part of a larger EEG based project where we are introducing different categories of distractions that are being

considered alongside the development of a taxonomy. These results should give some insight into the

practicability of the current P300 speller to be used for real-world applications.

1 INTRODUCTION

In this paper, we analyze the effect that an auditory

distraction, explicitly that of digital music, with

different levels of intensity in regards to volume (off,

low, mid, high) have on the accuracy, amplitude,

latency, user preference, signal morphology, and

overall signal quality. Our research makes use of non-

invasive Brain-Computer Interface (BCI) on the basis

of Electroencephalography (EEG). The work

presented here is part of a larger EEG based project

and in continuation of our previous papers (Schembri

et al., 2017); (Schembri et al., 2018); (Schembri et al.,

2018); (Schembri et al., 2019).

Event-related potentials (ERPs) are slow voltage

fluctuations or electrical potential shifts recorded

from the nervous system. These are time-locked to

perceptual events following a presentation of a

stimulus being either cognitive, sensor or motor

stimuli. The simplest paradigm for eliciting an ERP is

by focusing attention on the target stimuli (occurs

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infrequently) embedded randomly in an array of non-

targets (occurs frequently). The methodology used

derives from the oddball paradigm; first used in ERPs

by Nancy, Kenneth and Steven (Squires et al., 1975),

where the subject is asked to distinguish between a

common stimulus (non-target) and a rare stimulus

(target). The target stimuli elicit one of the most

renowned ERP components known as P300, which is

an exogenous and spontaneous component and was

first described by Sutton (Sutton et al., 1965). The

name is derived from the fact that it is a positive wave

that appears around 300ms after the target stimulus.

Unless otherwise noted herein, the term P300 (P3)

will always refer to a visual P300b (P3b) which is

elicited by task-relevant stimuli in the centro-parietal.

BCI research and development has been

predominantly focused on speed and accuracy of the

BCI application but has been wanting in usability,

such as the environment in which it is being used. In

fact, many BCI applications and experiments were

and are still being performed in laboratory settings

Schemrbi, P., Pelc, M. and Ma, J.

The Effect That an Auditory Distraction with Differing Levels of Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity Equipment: Alongside the Development of a Taxonomy.

DOI: 10.5220/0008065200500058

In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 50-58

ISBN: 978-989-758-376-6

with unrealistic conditions, where the subject sits in a

sound-attenuated room without any distractions

(Kam et al., 2019) (Bradford et al., 2018). Only a few

research papers such as (Nam et al., 2010) and

(Valentin et al., 2019) focus on real-world contexts,

however they were either using medical grade

equipment (Oliveira et al., 2016) and/or focusing on

auditory ERPs (Zink et al., 2016).

The need for this study originated to broaden the

utilization of this technology for both healthy subjects

and especially to those individuals with severe

neuromuscular disabilities (Sellers, et al., 2006), by

providing a solution based on low fidelity equipment

which is assessed outside lab conditions, and into

noisy environments. Our null hypothesis based on

preceding related and tantamount medical grade

research are that this type of distraction does not show

any statistically significant effect on accuracy, task

performance, amplitude, or latency.

In this work, we report a study where ten healthy

subjects used Farwell and Donchin P300 speller

paradigm in conjunction with the xDAWN algorithm

(Rivet et al., 2009) while utilizing low fidelity

equipment. The subjects were asked to communicate

five alphanumeric characters, referred to as symbols,

in each of the four separate scenarios i.e. off, low, mid

and high volume. The main goal for this study was to

methodically investigate the usability of a P300 BCI

system, explicitly that of a P300 Speller, in a specific

context. Empirical experiments were conducted to

assess how environmental factors such as music, with

different levels of intensity, affect the task

performance and quality of P300 component. This

work is part of a larger EEG based project where we

are introducing different categories of distractions

which are being considered alongside the

development of taxonomy as introduced in Figure 1.

This paper is structured as follows: the equipment,

participants and experimental procedures are

described in Section 2. The offline and online ERP

results are presented in Section 3. Conclusions and

future work are given in Section 4.

Figure 1: Development of extendable Hierarchal Taxonomy

of Distractions for P300b.

2 METHODOLOGY

The following segment/s of the methodology are the

author's previous work as referenced above and are

adopted and outlined in the current paper for readers’

convenience.

2.1 Low Fidelity Hardware

The work reported herein is based on an OpenBCI 32-

bit board (called Cyton) connected with an Electro-

Cap using the international 10/20 system for scalp

electrode placement in the context of EEG

experiments. The Cyton board’s microcontroller is

the PIC32MX250F128B with a 32-bit processor and

a maximum speed of 50MHz; storage of 32KB of

memory and is Arduino compatible. The board uses

the ADS1299 IC developed by Texas Instruments,

which is an 8-Channel, 24-Bit, simultaneous

sampling delta-sigma, Analogue-to-Digital Converter

used for biopotential measurements. The system

comes with a pre-programmed USB dongle for

wireless communication which communicates with

the low-cost RFDuino RFD22301 microcontroller

built on the Cyton board. An additional feature which

is included in the board is a 3-axis accelerometer from

ST with model LIS3DH. This can prove to be quite

useful; such as, for sensing a change in orientation of

the head or sensing rough motion. A more thorough

explanation of the hardware components of the Cyton

board can be found in our previous paper (Schembri

et al., 2017). The Electro-Cap being used in our

experiments has the fabric which is made from elastic

spandex and has recessed pure tin wet electrodes

directly attached to the fabric. The term wet

electrodes type implies that the use of an electrolyte

gel is required to make effective contact with the

scalp otherwise it may result in impedance instability.

A pair of Creative Labs SBS 15 speakers were

used to output the three levels of music. The speakers

have a nominal (RMS) output power of 5 Watt per

speaker, a frequency response of 90Hz – 20,000Hz

and a signal to noise ratio of 90dB.

2.2 Participants

We enlisted a total of N = 10 healthy subjects, seven

males and three females, aged 29-38 (M = 33.8)

which voluntarily participated in this study. Nine out

of the ten subjects’ native language was Maltese and

the tenth subject’s native language was English. All

subjects spoke fluent English and were familiar with

the symbols displayed on the P300 Speller. All

The Effect That an Auditory Distraction with Differing Levels of Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity

Equipment: Alongside the Development of a Taxonomy

subjects had previous experience using BCI and

formerly performed P300 speller experiments.

One other subject assisted in the initial testing and

configuration of the equipment; however, he/she did

not take part in the official experiment and hence is

not included in the results.

2.3 Data Acquisition

The EEG signals were sampled at 250Hz, while the

sampling precision was 24-bit. The recordings were

stored anonymously as raw data in OpenVIBE .ov

format. These were later converted to a comma-

separated value (CSV) files for offline analysis. Eight

EEG electrodes were used in different regions of the

scalp according to the International 10-20 System.

The electrode positions C3, Cz, C4, P3, Pz, P4, O1

and O2 were selected as shown in Figure 2. This is

because the spatial amplitude dispersal of the P300

component is symmetric around Cz and its electrical

potential is maximal in the midline region (Cz, Pz)

(Ogura et al., 1995). A referential montage was

selected with the reference electrode being placed on

the left earlobe A1 given that, in general, a mastoid or

earlobe reference will produce a robust P300

response. The right ear lobe A2 was used as ground.

The electrodes are referenced to electrode A1 as

follows: Ch1: C3; Ch2: Cz; Ch3: C4; Ch4: P3; Ch5:

Pz; Ch6: P4; Ch7: O1; Ch8: O2.

Figure 2: Electrode placement following the 10-20 system.

2.4 P300 Speller and xDAWN

In this paper, we make use of Farwell & Donchin

P300 speller, which is based on visual stimuli, in

conjunction with the xDAWN algorithm. Figure 3

depicts what is presented to the subject i.e. a six by

six grid which is made up of thirty-six alphanumeric

characters referred to as symbols. In this

methodology, each row and column of the spelling

grid is augmented in random order and the subject is

asked to distinguish between a common stimulus

(nontarget) and a rare stimulus (target). As a result of

the (target) stimuli, an exogenous and spontaneous

ERP potential known as P300; which is a positive

deviation around 300ms after the stimuli; is evoked in

the brain. The desired symbol is determined and

predicted by the intersection of the (target) row and

column. This prediction entails distinguishing

between non-target i.e. rows/columns stimuli that do

not generate a P300 component and target i.e.

row/column stimuli that generate a P300 component.

In any recorded EEG signal, the P300 component

which has a typical peak potential between 5-10µV,

is embedded and masked by other brain activities

(typical EEG signal +-100µV) such as muscular

and/or ocular artefacts (Schembri et al., 2017) leading

to a very low Signal-to-Noise Ratio (SNR) of the

P300 component. This indicates that it would be very

difficult to detect the target stimuli from a single trial,

which is denoted by a series of augmentation, in

random order, of each of the six rows and six columns

in our matrix (i.e. twelve augmentations per trial). A

popular way to address the limited SNR of EEG is for

each symbol to be spelled numerous consecutive

times and the respective column/row epochs are

averaged over a number of trials, thus canceling

components unrelated to stimulus onset.

The xDAWN process of spatial filtering is (1) a

dimensional reduction method that creates a subset of

pseudo-channels (referred to as output channels) by a

linear combination of the original channels and (2) it

promotes the appealing part of the signal, such as

ERPs, with respect to the noise. This is applied to the

data before performing any classification such as

LDA (Linear Discriminant Analysis) which was used

in this paper. A more thorough explanation of the

xDAWN algorithm can be found in our previous

paper (Schembri et al., 2018) or (Rivet et al., 2009).

Figure 3: BCI “P300 Speller”. The screen as shown to the

subjects with the 3rd row highlighted.

2.5 Experimental Design

In this study, there was one independent variable

manipulated: (a) digital music (off, low, medium and

high) within-subjects variables. In addition, there

CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications

were several dependent measures used which can be

categorized into three types of dependent variables:

online performance (accuracy), offline performance

(amplitude and latency) and user preference.

2.5.1 Independent Variables

Four-levels of digital music were employed to

represent different real-world scenarios: ‘off’ versus

‘low’ versus ‘medium’ versus ‘high’. The ‘off’ level

cited as M0 represents a lab condition scenario, where

subjects are seated in a sound-attenuated room. The

‘low’ level volume cited as M30 was set at thirty

percent i.e. between 20 and 30dB which simulates

background music. The ‘medium’ level volume cited

as M60 was set at sixty percent i.e. between 50 and

60dB which simulated active listening to a movie.

The ‘high’ level volume cited as M90 was set at

ninety percent i.e. between 80 and 90dB which

simulated disco level music only i.e. no crowd chatter

or noise. This first experiment i.e. M0 was done as a

basis for comparison for M30, M60, and M90.

2.5.2 Dependent Variables

Online Performance (Accuracy): is the number of

correctly spelled symbols over the number of planned

target symbols to be spelled; in our case 5 symbols

which make up the word BRAIN.

Offline Statistics (Amplitude and Latency): P300

Amplitude (μV) is related to the distribution of the

subject’s processing resources assigned to the task. It

is defined as the voltage difference between the

largest positive peak from the baseline within the

P300 latency interval. P300 Latency is considered a

measure of cognitive processing time, generally

between 300-800ms (Stern et al., 2001) poststimulus

i.e. after target stimulus. In simplest terms, it is the

time interval between the onset of the target stimulus

and the peak of the wave.

User Preference: Throughout a questionnaire, the

subjects were asked to rank from one to four, one

being the best and four being the worst, their favorite

usage condition.

2.6 Experimental Procedure

Each subject was invited and attended an induction

session which was aimed to re-educate all subjects on

the P300 speller paradigm and the hardware utilized.

The subjects’ were informed on the following: (1)

they would be performing the experiment in five

unique conditions, in sequence; (a) in the training

phase, in a sound-attenuated room i.e. lab conditions;

(b) M0, (c) M30, (d) M60, (e) M90, as explained in

the independent variable section; (2) the symbols to

spell were “BRAIN” for (1b) to (1e) and fifteen

random symbols for (1a). Any subjects’ query was

answered at this stage. Before the start of the

experiments, each subject was asked to relax for a few

minutes in a seated position. The subject was seated

approximately one meter away from the display. The

researcher and his equipment were situated on the left

side of the subject. The speakers were situated one

meter away and facing the subject at a 15-degree

angle. The experiment was started when the subject

was able to properly perform the task at hand and had

no additional questions. Prior to the start of every

experiment, the impedance of the electrodes was

confirmed to be less than 5KΩ.

The display presented to the subjects is shown in

Figure 3 where 36 symbols presented in a 6x6 matrix.

The target symbol was preceded by a cue i.e. one of

the symbols was highlighted in blue at the beginning

of the symbol run. Each row and column in the matrix

was augmented randomly for 100ms and the delay

between two successive augmentations was 80ms.

This led to an interstimulus interval (ISI) of 180ms.

For each symbol, six rows and six columns were

augmented for fifteen repetitions and there was a

100ms inter-repetition delay and a 300ms inter-trial

period between the end of the trials of one symbol and

the beginning of trials of the next symbol, which

allowed the subject to focus the attention on the next

symbol. At the end of each symbol run, the predicted

symbol was presented with a green cue, which

indicated whether the system predicted the correct

target symbol. The subjects were given a short break

between experiments.

The training phase (1a) consisted of one session

with 15 random symbols by 15 trials each (i.e. 12

flashes of columns/rows per trial * 15 trials = 180

flashes per symbol). The recording of the training

phase took approximately 10 minutes. The M0, M30,

M60 and M90 task experiments consisted of one

session each with the aforementioned conditions and

configurations while spelling the symbols “BRAIN”

consecutively. Similarly to the training phase, each

symbol had fifteen trials each. The recording of each

task lasted approximately 6 minutes. In total, there

were 15 symbols spelled in the training phase and 5

symbols spelled in each task, per subject. Hence due

to the matrix disposition, there were in total 2700

flashes in the training phase, amongst which 450 were

targets; and 3600 flashes in each task (900 * 4 tasks),

amongst which 600 (150 * 4 tasks) were targets.

These values are per subject. The data was stored

anonymously by referring to the subjects as subject1-

10 respectively.

The Effect That an Auditory Distraction with Differing Levels of Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity

Equipment: Alongside the Development of a Taxonomy

2.7 Signal Processing

The online system was controlled by OpenViBE 2.0.0

which is a C++ based software platform designed for

real-time processing of biosignal data. The

acquisition server interfaces with the Cyton board

and generates a standardized signal stream that is sent

to the designer which in turn is used to construct and

execute signal processing chains stored inside

scenarios. The signal was obtained via the acquisition

server which does not communicate directly with the

Cyton board. Instead, it provides a specific and

dedicated set of drivers that does this task. The signal

was obtained at a sampling rate of 250Hz with 8 EEG

channels and 3 accelerometer (auxiliary) channels.

The experimental paradigm was controlled by the

OpenViBE designer where a number of scenarios in

the “P300: Basic P300 Speller demo with xDAWN

Spatial Filter” were executed in succession.

In the offline analysis, the following procedure is

done for each M0, M30, M60, and M90. The captured

raw data was converted from the proprietary

OpenVIBE .ov extension to a more commonly used

.csv format using a particular scenario aimed for this

task. The converted data was later imported into

MATLAB R2014a and any unnecessary rows and

columns such as headers and auxiliary data were

removed. Next, we filtered out the data to include the

target stimulations with code (33285); non-target

stimulations (33286); and visual stimulation stop

(32780), which is the start of each flash of row or

column. Subsequently, we had to perform a signal

inversion due to the hardware and driver

implementation. The data (samples and event info)

were later imported into EEGLAB for offline

processing. The first process was to apply a bandpass

filter of 1-20HZ to eliminate the environmental

electrical interference (50Hz or 60Hz), to remove any

signal harmonics and unnecessary frequencies which

are not beneficial in our experiments and to remove

the DC offset. Next, the imported data was used in

ERPLAB which is an add-on of EEGLAB and is

targeted for ERP analysis. We took every event we

wanted to average together and assigned that to a

specific bin via the binlister. This contained an

abstract description of what kinds of event codes go

into a particular bin. In our experiments we have used

the following criteria: “.{33285}{t<50-150>32780}“

for the target and “.{33286}{t<50-150>32780}” for

the non-target. This implies that it is time-locked to

the stimuli event 33285 (target) or 33286 (non-target)

and must have the event 32780 that happens 50 to

150ms after the target/non-target event. If this criteria

is met, it is placed in the appropriate BIN; in our case

BIN1 for target and BIN2 for non-target. Next, we

extracted the bin-based epochs via ERPLAB (not the

EEGLAB version) and set the time period from -0.2s

before the stimulus until 0.8s after the stimulus. We

have also used baseline correction (pre) since we

wanted to subtract the average pre-stimulus voltage

from each epoch of data. Next, we passed all channels

epoch data through a moving window peak-to-peak

threshold artifact detection with the voltage threshold

set at 100μV, moving window width at 200ms and

window step at 100ms to remove unwanted signals

such as blinking and moving artifacts. Subsequently,

we averaged our dataset ERPs to produce part of the

results shown in Table 5. Lastly, we performed an

average across ERPsets (Grand Average) to produce

the results in Figure 3 and part of the results in Table

5. The data for Table 5 were generated by the ERP

measurement tool. A more thorough explanation on

segments of the signal processing can be found in our

previous paper (Schembri et al., 2018).

3 RESULTS

In this section, we present several results in relation

to the dependent variables such as a one-way

ANOVA (factorial analysis) to determine the effect

that off, low, mid and high level of volume intensity

have on the online performance (accuracy), offline

statistics (amplitude and latency) and user preference.

In the following tables the labels M0, M30, M60, and

M90 represent “no music - lab condition”, “music at

30%”, “music at 60%” and “music at 90%” volume

respectively. Moreover, M0, M30, M60, and M90

might be interchangeably referred to as BIN1, BIN3,

BIN5, and BIN7 respectively.

3.1 Online Analysis

Following the online experiments, the results

achieved per subject are shown in Table 1 which

depicts the correct symbols predicted out of five (i.e.

symbols BRAIN) and the percentage in parentheses,

rounded to the nearest one, for the accuracy

dependent variable. It must be noted that in an

incorrect symbol prediction, it might be the case that

the column was predicted correctly, whilst the row

was predicted incorrectly or vice versa. For instance,

subject8 had a success rate of 80% in the M30

scenario, with the symbol R predicted as symbol Q

i.e. the row prediction was correct but not the column.

However to avoid ambiguity we have decided to

assume that both row and column prediction were

incorrect when the symbol is predicted incorrectly.

CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications

Table 1: Symbols spelled (out of 5) and percentage (in

parentheses) for the accuracy dependent variable.

Subject LAB M30 M60 M90

5 (100%) 5 (100%) 4 (80%) 5 (100%)

5 (100%) 5 (100%) 4 (80%) 4 (80%)

5 (100%) 5 (100%) 5 (100%) 5 (100%)

5 (100%) 4 (80%) 5 (100%) 5 (100%)

S10

5 (100%) 5 (100%) 5 (100%) 5 (100%)

Grand

Avera

100%

96%

98%

We have performed a one-way ANOVA which is

based on our independent variable with four

levels/groups (M0, M30, M60, and M90) as presented

in Table 2, to determine if there is a significant

difference between the four means of each group or if

they are all the same. We have chosen to use a 5%

significance level (0.05) denoted as α (alpha) and

rounded all values to the nearest thousandth. Our null

hypothesis (H

) states that the means are all equal i.e.

the mean of M0, M30, M60, and M90 are all the

same. Our alternate hypothesis (H

) states that at least

two of these means are different.

Table 2: One-way ANOVA test on Accuracy.

Source of

Variation

SS df MS F

P-

value

crit

Between

Groups

0.275 3 0.092 0.805 0.499 2.866

Within

Groups

4.1 36 0.114

Total 4.375 39

In the first column we have the source of

variation, where ANOVA carries out an analysis

between groups variation (i.e. M0, M30, M60, M90),

and also carry an analysis of the within groups

variation i.e. the variation within each of our four

groups (refer to Table 1). In the second column, we

have the sum of squares (SS) of the variation, which

is the spread between each individual value and the

mean. The third column is the degrees of freedom (df)

which is the (number of samples – 1). We have four

samples of between groups which gives three and we

have forty samples in total which give thirty-nine.

That allows us to calculate the within-group df which

is total less between groups i.e. a value of thirty-six.

The fifth column we have the mean Square Values

(MS) which is calculated by dividing the SS by the

corresponding df. The sixth column is the F statistic

which is the key statistic where we divide the MS

between groups by the MS within group. Since our F

statistic got a result of 0.805 which is smaller than our

F-critical value (8

column), this implies that we

accept the H

i.e. that all means are equal and reject

. Also, by analyzing that the P-value (7

column)

which is 0.499 i.e. it is greater than the alpha value of

0.05, so we can also accept H

and reject H

3.2 Offline Statistics

In this section, we process and analyze the averaged

epoch signal of ten subjects in relation to the

independent variable (LAB, M30, M60, and M90).

Figure 3 shows the grand average P300

component for all ten subjects in each scenario which

include all eight channels and an average channel

(AVG). It is comprised of the grand-averaged raw

signals i.e. (5 symbols with 15 trials per symbol); with

(12 flashes of columns/rows per trial); with (10

subjects) i.e. 9000 flashes amongst which 1500 were

targets. In addition figure 3 shows four overlapping

signals, (i) BIN1 - Target for M0 scenario shown in

black (solid for grayscale), (ii) BIN3 - Target for M30

in red (dash-dot) (iii) BIN5 - Target for M60 in blue

(dashed) and (iv) BIN7 - Target for M90 in green

(dotted). To avoid ambiguity and for clarity of the

illustration, we have omitted BIN2, BIN4, BIN6, and

BIN8 which represent the non-target signals.

Table 3 shows the means and standard deviations

in parentheses, for the dependent variables (amplitude

and latency) according to levels of the independent

variable rounded to the nearest hundredth. This data

includes the average of all eight recorded electrodes

throughout the five symbols and is shown per subject

for each BIN1, BIN3, BIN5, and BIN7.

We have performed a one-way ANOVA which is

based on our independent variable with four

levels/groups (Lab, M30, M60, and M90) for our

dependent variables (amplitude and latency) as

presented in Table 4 and Table 5 respectively. In

Table 4 which represents the amplitude, we can see

that the F statistic is 0.723 which is smaller than our

F-critical value of 2.866. In addition, our P-value is

0.545 which is greater than the alpha vale. This

implies that we can accept the null hypothesis (H

)

and reject the alternate hypothesis (H

). In Table 5

which represents the latency, we can see that the F

statistic is 2.982 which is slightly larger than our F-

critical value of 2.867. In addition, the P-value is

0.044 which is slightly smaller than the alpha value.

This implies that we reject H

and accept H

. A more

thorough explanation of the one-way ANOVA can be

found in the previous section.

The Effect That an Auditory Distraction with Differing Levels of Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity

Equipment: Alongside the Development of a Taxonomy

Table 3: Means and Standard Deviations (in Parentheses) for Two Dependent Measures (Amplitude and Latency).

Subject

LAB

(BIN 1)

M30

(BIN 3)

M60

(BIN 5)

M90

(BIN 7)

Amplitude

(μV)

Latency

(ms)

Amplitude

(μV)

Latency

(ms)

Amplitude

(μV)

Latency

(ms)

Amplitude

(μV)

Latency

(ms)

4.90

(

0.50

)

466.5

(

2.97

)

2.09

(

0.28

)

473.5

(

12.46

)

3.90

(

0.40

)

470.5

(

5.21

)

2.88

(

0.40

)

462.5

(

19.00

)

2.39

(1.14)

437.0

(82.16)

3.24

(0.35)

434.0

(76.58)

4.26

(0.41)

419.5

(54.15)

3.66

(0.83)

416

(66.69)

3.29

(1.68)

431.0

(83.52)

2.18

(2.24)

417.0

(71.61)

4.03

(0.99)

430.0

(85.68)

3.64

(1.42)

423

(78.49)

3.70

(

0.98

)

466.0

(

107.48

)

4.95

(

0.54

)

427.0

(

82.11

)

4.14

(

0.45

)

432.5

(

86.76

)

3.25

(

1.08

)

419.5

(

75.49

)

3.96

(

0.78

)

444.0

(

91.49

)

4.11

(

0.95

)

431.0

(

84.58

)

4.61

(

0.76

)

428.5

(

80.21

)

4.77

(

1.17

)

436.5

(

85.56

)

5.39

(1.64)

483.0

(1.85)

3.63

(2.44)

479.0

(17.73)

4.99

(2.40)

484.5

(3.96)

3.70

(2.12)

495

(2.83)

1.70

(

1.66

)

430.5

(

81.75

)

4.94

(

1.26

)

431.5

(

84.97

)

4.18

(

1.24

)

427.5

(

79.81

)

4.58

(

1.34

)

449.5

(

93.64

)

2.64

(

1.38

)

415.0

(

71.10

)

2.51

(

1.57

)

440.0

(

72.88

)

3.52

(

1.22

)

415.5

(

66.91

)

2.18

(

1.69

)

446.0

(

90.16

)

4.46

(1.94)

439.0

(85.89)

2.50

(1.40)

415.5

(71.66)

2.17

(2.05)

424.0

(76.67)

3.67

(1.89)

411.0

(75.46)

S10

3.58

(0.26)

497.5

(87.40)

4.08

(0.71)

420.5

(80.61)

3.46

(0.61)

420.5

(76.98)

3.51

(0.46)

424.5

(84.43)

Grand

Avera

3.60

(

1.83

)

440.0

(

85.31

)

3.43

(

1.66

)

429.0

(

80.87

)

3.93

(

1.38

)

427.5

(

79.98

)

3.59

(

1.46

)

431.0

(

81.04

)

Figure 3: Grand average P300 for all 10 subjects in each scenario with all eight channels and an average channel (AVG).

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

AVG

-200 200 400 600

-5

-3.8

-2.5

-1.3

1.3

2.5

3.8

BIN1: Target - Lab

BIN3: Target_Music30

BIN5: Target_Music60

BIN7: Target_Music90

CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications

Table 4: One-way ANOVA on Amplitude.

Source of

Variation

SS df MS F

P-

value

crit

Between

Grou

2.798 3 0.933 0.723 0.545 2.866

Within

Grou

46.415 36 1.289

Total

49.212 39

Table 5: One-way ANOVA on Latency.

Source of

Variation

SS df MS F

P-

value

crit

Between

Grou

3236.4 3 1078.8 2.982 0.044 2.867

Within

Groups

13025.6 36 361.822

Total

16262 39

3.3 User Preference

Exactly after the experiments were finished, each

subject was presented with two questionnaires (a) and

(b) to specify their preferred BCI usage condition.

The ranking consisted of maximum weight value of

four (4) as the most desired and minimum weight

value of one (1) for the least desired.

In the first questionnaire, the subjects were

allowed to give the same ranking to different groups

as shown in column (a) of Table 6. Expectedly, the

M0 scenario got the highest ranking, whilst

surprisingly M90 came second, M60 third and M30

last. The frequency analysis grouped by the highest

value of four (4), shows that the M0 was given 100%,

followed by M90 with 50% trailed by M30 and M60

equally at 40%.

In the second questionnaire, the subjects were

asked to give a unique ranking (1-4) to each scenario

as shown in column (b) of Table 6. The results are

similar to those achieved in the questionnaire (a),

where M0 came first, followed by M90, M60 and M30

respectively. The frequency analysis shows that M0

got 40%, followed by M90 at 23%, M60 at 22% and

lastly by M30 at 15%.

Our assumption on why M90 placed second and

M30 placed last in both questionnaires is that, since

the experiments were performed in sequence, the

subjects were staggered by the difference between M0

and M30, while they gradually got accustomed to the

music and hence they were not bothered or distracted

as they initially were. In addition, the results indicated

that the user preference wasn’t affected by the

loudness of the music.

Table 6: User preference for two questionnaires with (a)

allowing the same ranking and (b) unique ranking.

Subject

LAB M30 M60 M90

(

)

(

)

(

)

(

)

(

)

(

)

(

)

(

)

4 4 3 2 3 1 4 3

4 4 4 3 3 1 3 2

4 4 3 1 3 3 3 2

4 4 3 1 3 2 4 3

4 4 4 3 4 2 3 1

4 4 4 1 4 3 4 2

4 4 4 1 4 2 4 3

4 4 2 1 3 2 3 3

4 4 2 1 3 3 4 2

S10

4 4 3 1 4 3 3 2

Total

40 40 32 15 34 22 35 23

4 CONCLUSION

In this study, N = 10 healthy subjects were able to

perform several experiments using Farwell &

Donchin P300 speller in conjunction with the

xDAWN algorithm, with a six by six matrix of

alphanumeric characters, in M0, M30, M60, and M90

environments while utilizing low fidelity equipment.

The goal of this study was to investigate the

usability of a visual P300 Speller, and assess the

extent of effect that an auditory distraction such as

digital music, with varying level of intensities (off,

low, mid, high), have on the user and overall BCI

performance (i.e. the dependent variables: accuracy,

amplitude, latency, and user preference). This study

is part of a larger-based project where we are

introducing different categories of distractions which

are being considered alongside the development of a

taxonomy and give some insight on the practicability

of real-world application of the current P300 speller

with our aforementioned low-cost equipment.

Our null hypothesis based on preceding related

and tantamount medical grade research was that this

type of distraction as elucidated in the independent

variable does not show any statistically significant

effect on the accuracy, amplitude, and latency

dependent variables. The results of a one-way

ANOVA factorial analysis accepts our null

hypothesis for the accuracy and amplitude dependent

variables, however, it was rejected for the latency

dependent variable since there was a minor statistical

significance as shown in the results section.

Non-statistical results show that the dependent

accuracy variable was highest in the M0 (100%) and

surprisingly followed by M90 (98%) trailed by M30

and M60 equally at 96%. Our empirical evidence

The Effect That an Auditory Distraction with Differing Levels of Intensity Have on a Visual P300 Speller While Utilizing Low Fidelity

Equipment: Alongside the Development of a Taxonomy

suggests that the subjects got accustomed to the music

in the M90 environment since these were performed

in sequence as explained before. The dependent

variable amplitude was highest in the M60 (M=3.93,

SD=1.38) followed by M0 (M=3.60, SD=1.83), M90

(M=3.59, SD=1.46) and M30 (M=3.43, SD=1.66).

Additionally, the dependent variable latency was

shortest in M60, followed by M30, M90 and finally

M0 as shown in Table 3. It seems that there is no

correlation between amplitude and latency. Lastly,

the user preference evidently shows that all subjects

preferred the M0, followed by M90, M60, and M30 in

both questionnaires as shown in Table 6. This

enforces our previous empirical evidence that the

subjects seem to get acquainted with the music in the

fourth sequential experiment of M90 while they are

staggered by the difference between M0 and M30,

which follow each other. These results also indicated

that the user preference wasn’t affected by the

loudness of the music. Moreover, the signals were

morphological consistent in all four scenarios, even

though they did not yield identical P300 components.

In the future we plan to run the independent

variable levels (M0, M30, M60, M90) experiments in

a randomized order and not sequentially, to avoid the

results being affected by subjects accustomization to

the distraction. Another important point to take into

account in future experiments is the possible impact

of mental fatigue with and without the presence of

distractions during repetitive exercises.

Our main contribution is the comparative

assessment in terms of (a) accuracy, (b) amplitude, (c)

latency and (d) user preference, between the levels of

the independent variable. Our main goal is to provide

insight into the practicability of the current P300

speller to be used in concurrence with several

taxonomized distractions.

In this paper, we have introduced our expandable

hierarchical taxonomy as depicted in Figure 1. This

work is part of a larger EEG based project where we

are introducing different categories of distractions

which are considered alongside the development of

taxonomy while using low fidelity equipment. Our

investigation is concerned with the way in which

different types of distractions (e.g. audio, visual, with

differing intensity/regularity and engagement factor)

translate into a reduction of the signal quality and

amplitude, or any other change/distortion that occurs.

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CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications