Spatial Auditory BCI with ERP Responses to
Front–Back to the Head Stimuli Distinction Support
Zhenyu Cai∗ , Shoji Makino∗ , Tomasz M. Rutkowski∗,†,∗ TARA Center, University of Tsukuba, Tsukuba, Japan

E-mail: tomek@tara.tsukuba.ac.jp ( corresponding author)
Website: http://about.bci-lab.info† RIKEN Brain Science Institute, Wako-shi, Japan
Abstract—This paper presents recent results obtained witha new auditory spatial localization based BCI paradigm inwhich ERP shape differences at early latencies are employed
to enhance classification accuracy in an oddball experimental
setting. The concept relies on recent results in auditory neuroscience showing the possibility to differentiate early anterior
contralateral responses to the spatial sources attended to. We alsofind that early brain responses indicate which direction, front orrear loudspeaker source, the subject attended to. Contemporary
stimuli–driven BCI paradigms benefit most from the P300 ERP
latencies in a so-called “aha-response” setting. We show thefurther enhancement of the classification results in a spatial
auditory paradigm, in which we incorporate N200 latencies. The
results reveal that these early spatial auditory ERPs boost offline
classification results of the BCI application. The offline BCI
experiments with the multi-command BCI prototype support
our research hypothesis with higher classification results andimproved information transfer rates.

I. I NTRODUCTION
A brain computer interface (BCI) utilizes human neurophysiological signals to communicate with an external computeror a machine, without depending on muscle activity [1].
Particularly, in the case of patients suffering from amyotrophic
lateral sclerosis (ALS), this could help them to communicate
or to complete various daily tasks (control a computer or typemessages on a virtual keyboard, etc). This would create avery good option for ALS patients to communicate with theirfamilies, friends or caretakers by using only their brain waves.
Hitherto, many approaches have focused on visual modality
BCI applications. A visual modality BCI cannot be used by
ALS patients who, in the advanced stages of the disease, often
suffer from limited or lost sight. In this paper, we present theconcept of an auditory BCI based on spatial sound stimuli,
which we refer to in brief as saBCI (spatial auditory BCI).
The saBCI concept is based on a basic feature of the human
auditory brain pathway which is very sensitive to the localization of changing spatial auditory sources [2]. The auditory
pathway also has a very good temporal resolution, which is anadditional feature we would like to utilize in the saBCI design.
This will allow the decrease of the inter–stimulus interval (ISI)of the presented sound stimuli in comparison to vision based
applications [3].
The majority of contemporary BCI applications aim at the
P 300 response latency without consideration of the remaining
event related potential (ERP) ranges. In this paper, we compareand discuss N 200 response [4] suitability, and we show thatits utilization improves the final classification results.
As a first step, we propose to utilize the early ERP latency
based modulation related to the so-called “N 200 anterior contralateral” (N 2ac) response [4]. This response is characterized
by different shapes in the brain ipsilateral and contralateral
to targets ERPs. Our previous research also confirmed in [5]that the N 2ac setting improved the BCI classification accuracy. Using this setting allowed us to classify targets fromleft and right side loudspeaker sound sources, respectively.
Psychophysical experiments also confirmed that the subjects
had no discrimination problems related to auditory front–backconfusion. Another previous study, conducted by the authors,further confirmed the feasibility of utilizing front and rear
loudspeaker directions for the saBCI paradigm [6].
In this paper, we report on the new finding that the different
ERP shape at the N 200 latency supports the discrimination
between auditory stimulus targets attended to, originating fromfront and rear loudspeakers respectively. This new finding
allows us to identify the auditory stimulus direction to which
the subject attended (front or rear). We call this new finding“a N 200 front–rear” (N 2f r) response.
We also report on the design of a new saBCI experimental
paradigm based on the auditory spatial localization principle
as the informative cue with support of both the N 2ac andN 2f r ERP components elicited in the new experimental setup,
as depicted in Figure 1. Our hypothesis is that the new ERP
component shall improve the classification results and the final
information transfer rate (IT R) leading to better BCI usage
comfort in general.
Within the novel saBCI paradigm framework, the subjects
are asked, as in the usual oddball paradigm [1], to attend toand count the target stimuli from the instructed or intended
directions, while ignoring the others. The EEG signals arerecorded with a g.MOBIlab+ EEG amplifier by g.tec Medical Engineering GmbH, Austria. We use dry g.SAHARA
electrodes by the same producer, which further improve theinterfacing comfort, since there is no need to apply a conductive gel to the subject’s scalp. In order to decrease theunnecessary and signal quality degrading muscular movement
related electromyographic (EMG) noise on ERP responses, thesubjects are requested to minimize their eye, facial and body
movements in general during the experiments.

g.SAHARA electrodes. The sampling frequency was set to256 Hz, with a notch filter to reject the 50 Hz power line noise,
set to remove the EEG signal band in a range of 48 ∼ 52 Hz.
The auditory stimulus was presented through six loudspeakers distributed at an equal radius of one meter around thesubject’s head, as depicted in Figure 1. Three speakers at equal
distances were positioned to the front and rear of the head.
Two short white and pink noise stimulus bursts were used, as
described in the following section. All the experiments wereconducted in a silent, low reverberation room in order to limit
environmental noise interference.
A. The Offline saBCI Experimental Protocol

Fig. 1. The top panel depicts the novel localization paradigm for auditory
sources to the front and rear of the subject’s head, based on spatial soundstimuli. The bottom panel shows our stimulus presentation concept illustrated
in the time domain. Each stimulus was presented in our experiments for 30 ms
with 270 ms silent breaks, with an inter–stimulus interval (ISI) of 300 ms.

The remainder of the paper is organized as follows. In thenext section, the experimental setup and the novel paradigm
are described, together with EEG signal pre–processing steps.
Next, analysis and optimization procedures of the ERP at
N 200 and P 300 response latencies for all experimental subjects are described. Finally, classification and discussion of
IT R results conclude the paper, together with future research
directions.
II. M ETHODSThe EEG experiments to validate the proposed spatial
auditory BCI paradigm utilizing the N 200 and P 300 latency
responses were conducted in the Multimedia Laboratory of theLife Science Center of TARA at the University of Tsukuba,
Tsukuba, Japan. All the details of the experimental procedureand the research targets of this approach were explained to theseven human subjects, who agreed voluntarily to take part. The
EEG BCI experiments were conducted in accordance with The
World Medical Association Declaration of Helsinki - EthicalPrinciples for Medical Research Involving Human Subjects.
The experimental procedures were designed in accordancewith ethical committee guidelines of the institutions to which
the authors of this paper are affiliated. The EEG signals
were recorded by a g.MOBIlab+ EEG amplifier with six dry

The experimental hypothesis was that we would be able todistinguish from the ERP shape which direction left, right,
front or rear to the head, the subject attended to based on theN 2ac and N 2f r responses.
To test the hypothesis, we conducted a series of EEG
recording experiments in the offline BCI mode [1] (no instantfeedback or classification results given to the subjects). The
experiments were performed with the seven healthy subjects(six males and one female; age range 21 − 42 with a meanof 26.4 years). The experimental procedure was explained indetail to each subject and her/his written consent was obtained.
The subject was seated in the center of the experimental
studio and the dry EEG electrodes were attached to the scalp.
The subject’s chair was positioned in the middle of the six
surrounding loudspeakers. The elevation of the loudspeakers
was set at the subject’s ear level. A computer display with
instructions about the experiment was set in front of thesubject. The six loudspeakers were distributed in a circle withthree loudspeakers (1, 2, 5) positioned to the front at a 45◦
angular distance. The remaining three loudspeakers (3, 4, 6)
were located to the rear at the same angular distance (seeFigure 1). Four loudspeakers (1, 2, 3, 4) were used to test theN 2ac effect, and the other two loudspeakers (5, 6) were used
to confirm our hypothesis about N 2f r response.
The sound stimuli were presented in a random order, one
at a time from a single loudspeaker (one trial consisted ofthe delivery of a single target and five non-targets). We
decided to use two broadband noise stimulus types in order toutilize the two spatial localization mechanisms of the human
auditory pathway (the inter-aural time and level differences– ITD/ILD) [2]. The white and pink noise stimuli of 30 ms
lengths with 5 ms linear attack and sustain periods werechosen. The ISI was set to 300 ms. A single session consisted of the six single trials (6 targets from each direction,accompanied by 30 non–targets). The target direction in eachtrial was presented randomly together with five non–targets.
For each subject and each stimuli, we performed 15 sessions(all together 90 targets and 450 non-targets were delivered).
The target direction instruction was presented visually on acomputer display and the auditory stimulus was given from theloudspeaker which the subject was later to attend to. Before
each experiment the subjects were allowed a short practicesession to familiarize themselves with the spatial auditory
conditions.

pink noise
5

III. A NALYSIS OF ERP R ESPONSES IN O FFLINE BCIPARADIGM

A. EEG Preprocessing
The EEG signals captured by the dry electrodes were first
filtered digitally with the two 5th –order Butterworth high– andlow–pass filters with cutoff frequencies of 0.5Hz and 25Hz,
respectively.
The high–pass filtering removed very slow baseline drift
related artifacts, as well as slow eye movements related EMG
interference. The low–pass filter limited higher frequency
EMG artifacts related to subject body muscle movements.
Next the EEG signals were segmented creating the ERP
related epochs. Each epoch started 100 ms before each stimuli
onset and ended 700 ms after it.
In the next step, the eye movement artifact rejection was
carried out. Auditory spatial stimuli have been known to cause
uncontrolled eye movements in subjects [10], which in thecurrent approach were removed with a threshold value set at
80µV (signal amplitude level above the usual EEG activity).
The rejected epochs were not further processed, since in thecurrent approach, the emphasis was on the spatial paradigm
validation. An example in Figure 2 shows the averaged andartifact–removed P 300 responses to target and non-target
sound stimuli with standard error bars respectively.
B. The Optimization of the EEG Electrode Locations and ERP
Feature Extraction
In the previously reported research on the N 2ac phenomenon [4], the anterior cluster of electrodes sites F 3, F 7,

targetnontarget

EEG amplitude [μV]

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In many current auditory BCI applications, the focus isput on a binary classification of brain evoked responses totargets versus non–targets [7], [6], [8], [9]. The majority ofcontemporary BCI applications aim at the P 300 response
latency without consideration of the remaining ERP ranges.
Only one of the papers recently published has mentioned
the N 200 latency range as possibly useful in supportingclassification [9], but there is no comparison made so far with
the P 300 only related results, which we present in this paper.
Basically, the concept of adding the early latency N 2ac
or N 2f r responses is based on the concept contained in
our previous research [6] and recent publications by othergroups [4] about this ERP range modulation by ipsilateral vs.
contralateral stimulus spatial locations, which results in the
different ERP shapes. This difference confirms the feasibilityof utilizing the early N 200 response latency to improve
the target vs. non–target classification accuracy. In order toanalyze precisely the impact of the early ERP responses on
the saBCI paradigm classification, we propose to conduct three
separate analyses that compare results from:• the P 300 only based BCI classification;• the N 2ac response based improvement;• the further N 2f r related classification boosting.

4

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Fig. 2. Grand mean averaged ERP responses of the seven subjects. The upper
panel presents the grand mean averaged ERP responses to pink noise. The
lower panel depicts respective results obtained with white noise stimulus. The
red lines represent targets and the blue lines non–targets. All the results arepresented together with standard error bars. The differences between targets
and non–targets are obvious in the range of 300 ∼ 600ms (the so-called“aha” or P 300 response).

C3, T 7, F 4, F 8, C4, and T 8 was used, as in the 10/20
international system [11]. In our experimental setup, we selectthe F 5, F 6, C3, C4, P 5, and P 6 electrodes in order tohave additional responses from parietal cortices known togenerate ERPs related to spatial and P 300 responses [12].
Additionally, we show that the P 5 and P 6 sites are also useful
to differentiate the responses to lateral stimuli similarly to theContralateral

Target
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Fig. 3. Grand mean averaged ERP responses of all seven subjects and the six
electrodes are plotted separately in the top two panels. The top panel shows
the target, and the second from the top the non–target averaged responses,
respectively. The significant differences between the responses can be seen,
as visualized by color coding of the p-values obtained from the ANOVA test
(statistical significance at p < 0.05) in the third panel from the top. The
bottom panels present the Kolmogorov–Smirnov test. The EEG electrodes
F 5, F 6, C3, C4, P 5 and P 6 were used in the experiments.

Fig. 4. Grand mean average ERP responses of all seven subjects and thesix electrodes plotted separately in each panel in contralateral (top panel)vs. ipsilateral (second from the top panel) stimulus direction presentation
settings. The significant differences between the two responses can be seen,
as visualized by the color with p-values of ANOVA test and KS test results(statistical significance for p < 0.05) in the third panel and fourth panel,
respectively. EEG electrodes F 5, F 6, C3, C4, P 5 and P 6 were used in theexperiment.

left vs. right only comparison revealed by N 2ac.
An example in Figure 4 shows the averaged and artifact–
removed N 2ac responses to ipsilateral and contralateralsound stimuli, as confirmed by our experiments. The N 200
area responses presented are elucidated for ipsilateral andcontralateral targets.

In order to validate statistically the differences between
target and non–target responses, we conducted an analysis
of variance test (ANOVA) [13] and Kolmogorov Smirnov test(KS) (to compare the two class ERP distribution similarities).
We also used this method to analyze the two class ERP
distributions in the ipsilateral vs. contralateral, as well as

Target from frontal loudspeaker
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[μV]

P5

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200 300 400 500
time [ms]600 700

Fig. 5. Grand mean averaged ERP responses of all subjects and the six
electrodes plotted separately in each panel for the front (top panel) and therear loudspeaker (second panel from the top) ERP responses. The significant
differences between the two responses can be seen, as visualized by the color
with p-values of ANOVA test and KS test results (statistical significance at
p < 0.05) in the third and fourth panels from the top, respectively. EEG
electrodes F 5, F 6, C3, C4, P 5 and P 6 were used in the experiment.

targets originating from the the front vs. rear loudspeakers,
respectively. The ANOVA and KS test methods were applied
to compare the differences in response distributions in single
trials for each sample point of the collected ERPs. As a result
we were able to extract discriminative information (in N 200
and P 300 latencies) leading to later classification optimization.
The results of target and non-target analyses are depicted in

Figure 3. The bottom panels in the above figure visualize the
ANOVA and KS test p−value results, which are the probabilityof the null hypothesis rejections that the distributions fromboth the ERPs compared are significantly different (usually inlife sciences, p < 0.05 is considered as a significant value).
The ANOVA test results panel in Figure 3 show that the P 300
related significant responses are located in the range from300ms to 500ms. The ERP analysis results of ipsilateral vs.
contralateral responses are shown in Figure 4. The p−values
obtained from ANOVA and KS tests and presented in Figure 4
reveal that the N 200 significant responses are located in
the range from 100ms to 300ms, similarly to the previously
published N 2ac phenomenon. This finding confirms our hypothesis that the early N 200–range latencies are related tospatial localization processes in the human brain and thatthe parietal electrodes also contribute to the result. The ERP
analysis results of the N 2f r experimental setting are depicted
in Figure 5. The p−values resulting from the ANOVA and KS
tests confirm that the significant N 200 response is located in
the range from 100ms to 200ms. This difference is a sourceof BCI classification accuracy improvement, discussed below.
In this paper, three types of binary classification related
feature extraction problems have been outlined so far:
1) First, we have evaluated ERP features separability in the
P 300 latency for classic target vs. non-target classification purposes.
2) Second, we have shown that the N 2ac latency has
further contributed to the ERP features separability inthe ipsilateral vs. contralateral stimulus presentation
setting.
3) Finally, we have postulated and validated the new concept of front vs. rear loudspeaker stimulus response
differences, which we named N 2f r.
We propose to identify the most discriminable features from
ERP responses using the ANOVA and KS tests describedabove, evaluating the statistical significance of the latency
differences. Next, a “hand picking” procedure could be applied
to only those ERP latencies within each subject’s ERP p–values that are smaller than a reasonable threshold of 0.20 as
depicted by blue shades in the third panels from the top andthe bottom panels in Figures 3, 4 and 5.
In the next section we show that the proposed method ofutilizing ANOVA and KS tests for p < 0.20 improves the
saBCI classification results when utilizing the N 200 latency
responses.
C. The Offline saBCI Classification
In order to test the proposed feature extraction method,the offline saBCI mode of classification was performed foreach subject separately, meaning that all the procedures wereconducted after each data collection experiment, without any
online feedback to the subjects. The classification procedure
was performed in a so-called binary task paradigm of targets
vs. non–targets, contralateral vs. ipsilateral, or front vs. rear
loudspeaker targets respectively.

In each classifier training and testing step, 90 targets and arandom subset of 90 non-targets were selected (from the 450
available) to have balanced numbers in the each class set. The
resulting chance level was 50%.
For the contralateral vs. ipsilateral response classification,
we selected 30 contralateral and 30 ipsilateral events.
To classify the targets from front vs. rear loudspeakers, weselected 15 targets from each direction respectively.
Based on our previous classification trials reported in [5],we decided to continue to use a Bayesian classifier, which
for the datasets in our case outperforms the linear discrimination analysis methods. Despite its simplicity, the NBC(naive Bayesian classifier) approach often outperforms moresophisticated classification methods [14], on the assumptionthat the individual ERP features are statistically independent.
It turns out that the NBC can be very robust, even to violationsof the independence assumption [14].
The results of the successful application of the NBC technique are presented in the next section.
IV. R ESULTS
As the outcome of the presented research we have obtained
results showing that for both the experimental settings of thesaBCI offline paradigm, the classic P 300 latency could be
improved with the N 2ac and N 2f r features identified withthe p−values calculated using the ANOVA or KS tests for
significance. We summarize below the results obtained.
A. The Classification Results from the P 300 ERP Latencies
in the Classic Oddball Paradigm Setting
The first summary of classification results is presented
in Table I, where classification accuracies for the features
drawn from P 300 are shown. All of the subjects performed
above the chance level of 50% for single feature latencies
of P 300 based on ANOVA and KS tests p−values. The
proposed “hand–picked” feature set identification using the
ANOVA and KS tests of significant ERP samples allowed
us to boost the classification results (in both cases with the
maximum classification boost of 29%) using leave–one–out
cross validation [14] for the NBC technique.
B. The Classification Results from the N 2ac ERP Feature in
the Ipsilateral vs. Contralateral Settings
The results of the approach to compare ipsilateral andcontralateral to target evoked potentials are summarized inTable II, based on the ERP features drawn from results of the
ANOVA and KS tests depicted in Figure 4. The classification
accuracy results were boosted 22% in the best case withANOVA test based feature extraction. With the KS test, theclassification accuracy results were even 28% enhanced in thebest case. In both the enhancement cases, the same methodof the NBC leave–one–out cross validation classification wasapplied.

C. The Classification Results of the N 2f r ERP Feature Extraction Method
The classification results of the proposed approach to compare target stimuli originating from front and rear loudspeaker
are summarized in Table III, based on the ERP features
selection from results of the ANOVA and KS tests analysis
depicted in Figure 5. The classification accuracy results inthis case were also 26% boosted.
TABLE IT HE CLASSIFICATION RESULTS FOR ERP LATENCIES IN
P 300 RESPONSES FOR THE target VS . non–target
PARADIGM . T HE THREE SETS ( WHOLE ERP, P 300
LATENCIES OPTIMIZED BY THE ANOVA METHOD AND
KS METHOD ) CLASSIFICATION RESULTS ARE COMPARED .
noise
conventional
ANOVA
KS
subject
stimulus
whole ERP
P300
P300
type
[%]
[%]
[%]
pink
61
71
71
#1
white
65
68
67
pink
61
85
78
#2
white
63
72
71
pink
58
62
65
#3
white
55
59
67
pink
49
68
53
#4
white
55
84
79
pink
48
69
82
#5
white
56
83
81
pink
55
70
66
#6
white
57
75
64
pink
54
82
83
#7
white
65
81
72

TABLE II
T HE CLASSIFICATION RESULTS FOR ERP LATENCIES IN
N 200 RESPONSES FOR THE ipsilateral VS . contralateral
PARADIGM . T HE THREE FEATURE SETS ( WHOLE ERP(0ms − 700 MS ), N 200 LATENCIES OPTIMIZED BY THE
ANOVA METHOD AND KS METHOD ) CLASSIFICATIONRESULTS ARE COMPARED .
noise
conventional
ANOVA
KS
subject
stimulus
whole ERP
N200
N200
type
[%]
[%]
[%]
pink
51
58
57
#1
white
35
51
56
pink
46
57
58
#2
white
38
64
59
pink
51
61
57
#3
white
53
60
65
pink
52
67
59
#4
white
51
67
71
pink
55
60
58
#5
white
38
60
52
pink
41
51
52
#6
white
43
48
57
pink
53
57
65
#7
white
45
65
73

D. Analysis of Information Transfer Rate Improvement Results
The amount of information carried by every selection in
the BCI application is usually quantified by the ITR, which is
calculated based on bits per selection R, defined as in [7]:
1−C
R = log2 N + C · log2 C + (1 − C) · log2
, (1)
N −1

TABLE III
T HE CLASSIFICATION RESULTS FOR ERP LATENCIES IN
N 200 RESPONSES FOR THE targets from front
loudspeaker VS . targets from rear loudspeaker
PARADIGM . T HE THREE FEATURE SETS ( WHOLE ERP (0 − 700 MS ), N 200 LATENCIES OPTIMIZED BY THE
ANOVA METHOD AND KS METHOD ) CLASSIFICATIONRESULTS ARE COMPARED .
noise
conventional
ANOVA
KS
subject
stimulus
whole ERP
N200
N200
type
[%]
[%]
[%]
pink
46
52
50
#1
white
51
63
77
pink
40
66
53
#2
white
36
51
56
pink
52
65
58
#3
white
51
55
67
pink
63
73
70
#4
white
53
60
57
pink
43
57
56
#5
white
46
57
51
pink
37
56
58
#6
white
52
62
56
pink
57
68
65
#7
white
44
51
55

TABLE VT HE ITR OF N2 AC , SEE EQUATIONS (1) AND (2), FOR
THE THREE ERP SETS RELATED CLASSIFICATIONAPPROACHES USING wholeERP , N 200 EXTRACTED
WITH ANOVA AND KS.
noise
Whole
ANOVA
KS
subject
stimulus
ERP
N200
N200
type
[bit/min][bit/min][bit/min]
pink
0.03
1.85
1.42
#1
white
0.00
0.03
1.04
pink
0.00
1.42
1.85
#2
white
0.00
5.73
2.35
pink
0.03
3.52
1.42
#3
white
0.26
2.90
6.59
pink
0.12
8.51
2.35
#4
white
0.03
8.51
13.13
pink
0.72
2.90
1.85
#5
white
0.00
2.90
0.12
pink
0.00
0.03
0.12
#6
white
0.00
0.00
1.42
pink
0.26
1.42
6.59
#7
white
0.00
6.59
15.85
TABLE VI
T HE ITR OF TARGETS FROM FRONT LOUDSPEAKER AND
TARGETS FROM REAR LOUDSPEAKER , SEE
EQUATIONS (1) AND (2), FOR THE THREE ERP SETSTABLE IV
T HE ITR OF TARGET VS . NON - TARGET, SEE
EQUATIONS (1) AND (2), FOR THE THREE ERP SETS

RELATED CLASSIFICATION APPROACHES USING

wholeERP , N 200 EXTRACTED WITH ANOVA AND KS.
noise
Whole
ANOVA
KS
subject
stimulus
ERP
N200
N200
type
[bit/min][bit/min][bit/min]
pink
0.00
0.12
0.00
#1
white
0.03
4.93
22.19
pink
0.00
7.52
0.26
#2
white
0.00
0.03
0.72
pink
0.12
6.59
1.85
#3
white
0.03
0.72
8.51
pink
4.93
15.85
11.87
#4
white
0.26
2.90
1.41
pink
0.00
1.42
1.04
#5
white
0.00
1.42
0.03
pink
0.00
1.04
1.85
#6
white
0.12
4.19
1.04
pink
1.42
9.56
6.59
#7
white
0.00
0.03
0.72

RELATED CLASSIFICATION APPROACHES USING

wholeERP , P 300 EXTRACTED WITH ANOVA AND KS.
noise
Whole
ANOVA
KS
subject
stimulus
ERP
P300
P300
type
[bit/min][bit/min][bit/min]
pink
3.25
13.13
13.13
#1
white
6.59
9.56
8.51
pink
3.52
39.02
23.98
#2
white
4.93
14.45
13.13
pink
1.85
4.19
6.59
#3
white
0.72
2.35
8.51
pink
0.00
9.56
0.26
#4
white
0.72
36.57
25.85
pink
0.00
10.68
31.99
#5
white
1.04
34.23
29.85
pink
0.72
11.87
7.52
#6
white
1.42
18.87
5.73
pink
0.46
31.99
34.23
#7
white
6.59
29.85
14.45

where C is the classification accuracy and N is the numberof classes (N = 2 in this paper). The final bit per minute rate
B is obtained after multiplication by a classification speed V ,resulting in bits per minute [bit/min] as:
B =V ·R(2)

The ITR results are summarized in Tables IV, V, and VI
respectively. For all the cases of the P 300, the N 2ac, andthe N 2f r, there were significant increases of the ITRs for the
majority of subjects.
V. C ONCLUSIONSIn this paper, we have presented three approaches leading
to the improvement of the classification accuracy and ITR in
the offline saBCI paradigm. The improvement was obtainedby introducing the novel ERP feature extraction methods in
the P 300 latency, the N 2ac and the N 2f r.
The first improvement has been presented in the form of
a comparison of classification rates for the three ERP featuresets of the P 300 latencies processed separately with ANOVA
and KS tests, versus the classic whole ERP features. The
classification accuracy was increased for all the subjects at
a maximum of 29% boost (ITR improvement at a maximumof 34 bit/min). This is a very good result, giving the possibility
to further improve the auditory based BCI paradigm.
The second improvement step was based on the N 2ac
concept. The classification obtained and ITR improvement arealso very encouraging.
The third improvement step was based on the N 2f r responses in the front–back to the subject head stimulus presentation. The classification and ITR improvement confirm that
our new findings can improve the final classification results.

The three main achievements reported in this paper allow us
to boost the accuracy of the novel saBCI paradigm in offlinemode, which is a step forward in non–vision based interfacing
strategies.
The results obtained reveal that not only the cortical auditory
information processing centers related to the cognitive streams
can be utilized for BCI purposes. Also the differences in
ERPs at early latencies before 300 ms are useful and theyguarantee good classification results and resulting ITR scores.
These results reveal that very early spatial auditory ERPs arepotentially interesting for faster BCI applications.
AUTHOR C ONTRIBUTIONS
Performed the EEG experiments and analyzed the data: ZC,
TMR. Conceived the concept of the spatial auditory BCI anddesigned the EEG experiments: TMR. Supported the project:
SM. Wrote the paper: ZC, TMR.
ACKNOWLEDGEMENTS
This research was supported in part by the Strategic Information and Communications R&D Promotion Program(SCOPE) no. 121803027 of the Ministry of Internal Affairs
and Communication in Japan, and by KAKENHI, the Japan
Society for the Promotion of Science, grant no. 12010738
and 24700154. We also acknowledge the technical support of
YAMAHA Sound & IT Development Division in Hamamatsu,
Japan.
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