Spatial Auditory BCI Paradigm Utilizing N200 andP300 Responses
Zhenyu Cai∗ , Shoji Makino∗ , Takeshi Yamada∗ , Tomasz M. Rutkowski∗†▽∗ TARA Center, University of Tsukuba, Tsukuba, Japan

E-mail: tomek@tara.tsukuba.ac.jp (▽ corresponding author)
Tel: +81-29-853-6261† RIKEN Brain Science Institute, Wako-shi, Japan

I. I NTRODUCTION

non–targets from series of event related potential (ERP) responses [4]. Recently a new result [5] was published elucidating “the 𝑁 200-anterior-contralateral” (𝑁 2𝑎𝑐) component
at the early latency (around 200ms) of an auditory ERP. The
𝑁 2𝑎𝑐 was obtained in an experiment using two 750ms longsound stimuli which were presented simultaneously from adifferent loudspeaker each. Subjects were requested to attended to the instructed target sound that could occur fromany loudspeaker.
We propose to design the new saBCI experimental paradigm
based on the auditory spatial localization principle as theinformative cues with support of the 𝑁 2𝑎𝑐 component elicitedin the new setup as depicted in Figure 1. Our hypothesis isthat the new ERP component shall improve the classification
results and the final information transfer rate (ITR) leading toa better BCI usage comfort.
Within the novel saBCI paradigm framework, the subjects

The brain computer interface (BCI) utilizes the humanneurophysiological signals to control an external computer ora machine [1]. BCI does not depend on muscle or peripheral
nervous system activities. Particularly, in case of amyotrophic
lateral sclerosis (ALS) suffering patients, it could help themto communicate or to complete various daily tasks (control
a computer or type messages on a virtual keyboard, etc).
This would create a very good option for ALS patients tocommunicate with their families, friends, or the caretakers
by only using their brain waves. So far, many approaches
are focusing on the visual modality BCI applications. The
visual modality BCI can not be used by ALS patients who
often suffer in the advanced disease stages from limited orlost sight. We present in this paper a concept of an auditory
BCI based on spatial sound stimuli, which we call shortly
saBCI (spatial auditory BCI). The saBCI concept is basedon a basic feature of the human auditory pathway which
is very sensitive to localization of changing spatial auditory
sources [2]. The auditory pathway has also very good temporal
resolution, which is an additional feature we would like toutilize in the saBCI design. This will allow to decrease stimuli
onset asynchrony (SOA) of the presented sound stimuli incomparison to vision based applications [3].
The contemporary stimuli–driven BCI approaches aremostly based on P300 responses to distinguish targets andFig. 1. The novel 𝑁 2𝑎𝑝𝑐 paradigm based on spatial sound stimuli

Abstract—The paper presents our recent results obtainedwith a new auditory spatial localization based BCI paradigm
in which the ERP shape differences at early latencies are
employed to enhance the traditional P300 responses in an oddball
experimental setting. The concept relies on the recent resultsin auditory neuroscience showing a possibility to differentiate
early anterior contralateral responses to attended spatial sources.
Contemporary stimuli–driven BCI paradigms benefit mostly
from the 𝑃 300 ERP latencies in so called “aha-response” settings.
We show the further enhancement of the classification results inspatial auditory paradigms by incorporating the 𝑁 200 latencies,
which differentiate the brain responses to lateral, in relation to
the subject head, sound locations in the auditory space. The
results reveal that those early spatial auditory ERPs boost online
classification results of the BCI application. The online BCI
experiments with the multi-command BCI prototype support our
research hypothesis with the higher classification results and theimproved information-transfer-rates.

are asked, as in usual oddball paradigm, to attend and countthe target stimuli from the instructed or intended direction,
while ignoring the other. The EEG signals are recorded with
g.MOBIlab+ EEG amplifier by g.tec. We use the novel dryg.SAHARA electrodes by the same producer which further
improve the interfacing comfort, since there is no need toapply a conductive gel. In order to decrease the unnecessary
and signal quality degrading muscular movement related electromyography (EMG) noise on ERP responses, the subjects
are asked to minimize their eye, facial and body in general
movements during the experiments.
In our previous paper [6] we proposed a channel and ERP–
latency selection in order to improve the classification results.
At that time the 𝑃 300 response (so called “aha–response” atthe latency around 300ms elicited to the expected/instructed
target stimulus [7]) was the major feature used for the classification of the attended targets of the oddball paradigm. In thispaper, we introduce the early latencies around 200ms (𝑁 200
response) which precede the 𝑃 300. They shall improve thefinal classification rates of the saBCI application.
The objective of this paper is to test and confirm ourworking hypothesis that the auditory evoked response basedon 𝑁 2𝑎𝑐 paradigm should improve the saBCI application
classification rates based on the new lateral to the subject headstimuli responses analysis.
From now on the paper is organized as follows. In thenext section the experimental setup and the novel paradigm
is described together with EEG signals pre-processing steps.
Next the analysis and optimization procedures of the ERPs
at 𝑁 200 and 𝑃 300 response latencies for all experimental
subjects are described. Finally classification and ITR results
discussion 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 have been conducted in Multimedia Laboratory in
TARA Life Science Center at the University of Tsukuba,
Tsukuba, Japan. All the experimental procedure details andthis approach research targets have been explained to theseven human subjects who agreed voluntarily to attend. The
experimental procedures are designed in accordance withethical committee guidelines of this paper author affiliatedinstitutions. The EEG signals are recorded by the g.USBamp
EEG amplifier with the six dry g.SAHARA electrodes. The
sampling frequency is set to 256Hz with a notch filter to reject
the 50Hz power–line noise.
The auditory stimuli has been presented through six loudspeakers distributed with an equal radius of 1 meter aroundthe subject’s head as depicted in Figure 1. Three speakers withequal distances are positioned at each lateral side to the head.
Two short white– and pink–noise stimulus bursts are used as
described in the following section. All the experiments are
conducted in a silent and low reverberation room in order tolimit an interference of “an environmental noise.”

A. The Offline saBCI Experimental Protocol
The experimental hypothesis is that we shall be able todistinguish from the ERP shape which direction (left or right)
the subject attends based on the novel 𝑁 2𝑎𝑐 response analysis
method.
To test the hypothesis we conduct a series of EEG recording
experiments in the offline BCI mode [1] (no instant feedback
or classification results given to the subject). The experiments
are performed with the seven healthy subjects (six males andone female; age range 21 − 42 with the mean of 26.4 yearsold). The experimental procedure has been explained in detailto each subject and her/his consent has been obtained. The
subject is seated in the center of the experimental studioand the dry EEG electrodes are attached on the scalp. The
subject’s chair position is surrounded by the six loudspeakers.
The elevation of the loudspeakers is fixed to the subject’s ear
level. A computer display with experimental instruction is set
in front of the subject. The six loudspeakers are distributedon a circle with the three loudspeakers (1, 3, 5) positioned on
the left side with 45–degrees angular distance. The remaining
three loudspeakers (2, 4, 6) are located on the right side withthe same angular distances (see Figure 1).
The sound stimulus is presented in random order one at atime from a single loudspeaker (a single trial consists of adelivery of a single target and five non-targets). We decide touse two broadband noise stimulus types in order to utilize two
spatial localization mechanism of the human auditory pathway(the inter-aural time and level differences - ITD/ILD) [2].
The white– and pink–noise stimuli of 30ms lengths with
5𝑚𝑠 linear attack and sustain periods has been chosen. The
SOA is set to 300ms. The single session consists of the six
single trials (6 targets from each direction accompanied by 30
non–targets). The target direction in each trial is presented
randomly together with five non–targets. For each subject
and each stimuli we perform 15 sessions (all together 90
targets and 450 non-targets are delivered). The target direction
instruction is presented visually on a computer display andauditory from the same loudspeaker which subject shall latterattend to. Before each experiment the subjects are allowed fora short practice session to familiarize themselves with spatial
auditory conditions.
III. T HE A NALYSIS OF ERP R ESPONSES IN O FFLINE BCIPARADIGM
In many current auditory BCI applications the focus isput on a binary classification of brain evoked responses totargets versus non–targets [4], [6], [8], [9]. The majority of
the contemporary BCI applications aim at the 𝑃 300 response
latency without consideration of the remaining ERP ranges.
Only a single of recently published papers mentions the 𝑁 200
latency range as possibly useful to support classification [9],but there is no comparison made so far with 𝑃 300 only
related results, what we attempt in this paper. We compareand discuss the 𝑁 200 response suitability and we show thatit really improves the classification results.

Grand maen ERPs

3

ipsilateral (target)
contralateral (target)
ipsilateral (non−target)
contralateral (non−target)

2

EEG amplitude [uV]

Basically a concept of adding the early latency 𝑁 2𝑎𝑐
response is based on our previous [6] research and the recently
published by other groups [5] concept of this ERP range
modulation by ipsilateral vs. contralateral stimulus spatial
locations. The ipsilateral 𝑁 2𝑎𝑐 response has higher amplitude
comparing to the contralateral one. This difference confirms
a feasibility to utilize the early 𝑁 200 response latency toimprove the target vs. non–target classifications outcomes.
In order to precisely analyze an impact of the early ERP
reposes on the saBCI paradigm classification we propose to
conduct two separate analyses that shall compare how muchthe improvement depends only on the 𝑁 200 response feature
addition, and how much on the new feature composition
based on the comparison of the ipsilateral and contralateral
responses as in 𝑁 2𝑎𝑐 design.

1

0

−1

A. EEG Preprocessing
The EEG signals captured by the g.MOBIlab+ system with
g.SAHARA dry electrodes are first filtered digitally with thetwo 5𝑡ℎ –order Butterworth high– and low–pass filters with
cutoff frequencies at 0.5Hz and 25Hz, respectively.
The high–pass filtering removes the very slow baseline
drift related artifacts as well as the slow eye movements
related EMG interferences. The low–pass filter limits thehigher frequency EMG artifacts related to subject body muscle
movements.
Next the EEG signals are segmented creating the ERP
related epochs. Each epoch starts 100ms before each stimuli
onset and ends 700ms after it. We use the 100ms pre–stimuli
onset interval for a baseline correction procedures.
In the next step the eye movement artifacts rejection iscarried out. Auditory spatial stimuli are known to cause insubjects the uncontrolled eye movements [10] which in thecurrent approach are removed with a threshold value set at
80𝜇V (signal amplitude level above the usual EEG activity).
The rejected epochs are not further processed, since in the current approach an emphasis is focused on the spatial paradigm
validation.
B. The Optimization of the EEG Electrode Locations and ERP
Features Extraction
In the previously reported research on 𝑁 2𝑎𝑐 phenomenon [5] the anterior cluster of electrodes sites 𝐹 3, 𝐹 7,
𝐶3, 𝑇 7, 𝐹 4, 𝐹 8, 𝐶4, and 𝑇 8 was used, as in 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 [7].
Additionally we show that the 𝑃 5 and 𝑃 6 sites are also useful
to differentiate the responses to lateral stimuli similarly as forleft–right only comparison revealed by 𝑁 2𝑎𝑐. We call the new
finding the 𝑁 2𝑎𝑝𝑐 (𝑁 200–anterior–posterior–contralateral) as
extension of the former one. An example in Figure 2 shows
the averaged and artifact–removed classical 𝑁 2𝑎𝑐 responses
to ipsilateral and contralateral sound stimuli as confirmed

−2

−3
−100

0

100
200
300
400
500
time after the stimuli onset [ms]

600

700

Fig. 2. The grand mean averaged ERP responses of the seven subjects. The
solid lines depict targets and the dashed ones non–targets. The red color
indicates ipsilateral and blue one the contralateral responses. The differences
between targets and non–targets are obvious after 300ms (the so called “aha”–
or 𝑃 300 response), while the lateral directions can be identified in 𝑁 200
latency area.

by our experiments. The presented 𝑁 200 area responses are
elucidated for ipsilateral and contralateral targets.
In order to validate statistically the differences between
target and non–target responses we conduct the t–test analysisof the two class ERP means [12] in ipsilateral vs. contralateralexperimental setting. The t–test method is applied to compare
the differences of response distributions in single trials foreach sample point of the collected ERPs. As the result we
can extract discriminative information (in 𝑁 200 and 𝑃 300
latencies) leading to later classification optimization. The
results of the above analysis are depicted in Figure 3. A color
bar located on a time scale in the above figure visualizes
the t–test’s 𝑝 value results, which is a probability of the null
hypothesis rejections that the means from the both compared
distributions are significantly different (usually 𝑝 < 0.05
in life sciences is considered as the significant value). The
color bar in the Figure 3 clearly shows that the postulated
𝑁 2𝑎𝑝𝑐 differential response for lateral responses is located in
the range from 100ms to 300ms, similarly to the previously
published 𝑁 2𝑎𝑐 one. This finding confirms our hypothesis,that the early 𝑁 200–range latencies are related to spatial
localization processes in the human brain and that the parietal
electrodes contribute also to the result.

Grand mean ERPs
4

0.35

targetnon−target
3

0.3

2

EEG amplitude [μV]

0.25

1
0.2

0
0.15

−1
0.1

−2
0.05

−3
−100

any online feedback to subjects. The classification procedure
is performed in a so called binary task paradigm (we classify
target vs. non–target, or contralateral vs. ipsilateral response
pairs each time only).
In each classifier training and testing step we select 90
targets and a random subset of 90 non-targets (from the 450
available) to have the balanced number of the members ineach class set. The resulting chance level is 50%. For the caseof the contralateral vs. ipsilateral responses classification weselect 30 contralateral and 30 ipsilateral events.
Based on our previous classification trials reported in [6]we decide to use a Baysian classifier, which outperforms
the linear discrimination analysis methods. The naive-Bayses
classifier (NBC) is particularly suited for the highly dimensional features. Despite its simplicity, the NBC approach often
outperforms more sophisticated classification methods [13].
The NBC application assigns an unknown sample (ERP features in our case) x = [𝑥1 , 𝑥2 , . . . , 𝑥𝑙 ]𝑇 based on probability
maximization to the class𝜔𝑚 = arg max
𝜔𝑖

0

100
200
300
400
500
time after the stimuli onset [ms]

600

700

Fig. 3. The grand mean averaged ERP for the all seven subjects and allelectrodes calculated together, while plotted separately for target (solid redline) and non–target (dashed blue line) responses. The significant differences
between the both responses can be found, as visualized by the color bar with𝑝-values of t–test results (statistical significance for 𝑝 < 0.05) in the bottom
part in the above panel, can be found around 200ms (𝑁 200 response latency)
and after 300ms (𝑃 300 response latency).

In this paper two types of binary classification problems are
discussed. First we evaluate our first hypothesis that adding
the early latency ERP periods as features improves the binary
target vs. non-target classification. Next we also show that thenovel 𝑁 2𝑎𝑝𝑐 response further enhances the results using theipsilateral vs. contralateral response comparison.
In order to find the most discriminable features from ERP
responses we use the results of the above described t–tests
evaluating statistical significance of them. We “hand pick”
only those samples within each subject’s ERPs for which the𝑝–values are smaller than 0.05 as depicted by blue shades of
the color bar at the bottom of the Figure 3. The significantly
different ERP samples of 𝑁 2𝑎𝑝𝑐 based experiments (we relax
here the condition to 𝑝 < 0.10 only) are depicted in Figures 4
and 5 for pink– and white–noise stimuli respectively. In thenext section we show that the relaxed condition of t–test’s
𝑝 < 0.10 improves already satisfactory the saBCI classification
results by incorporating the 𝑁 200 latency responses.
C. The Offline saBCI Classification
We perform the classification steps for each subject separately in saBCI offline mode, which means that all procedures
are conducted after each experiment of data collection, without𝑙∏𝑝(𝑥𝑗 ∣𝜔𝑖 ),𝑖 = 1, 2, . . . , 𝑀,

(1)

𝑗=1

with an assumption that the individual features 𝑥𝑗 , 𝑗 =1, 2, . . . , 𝑙, shall be statistically independent. It turns out that
the NBC can be very robust also to violations of the independence assumption [13].
Consider the vector x with features according to the valuesof the ERP “hand picked” samples. The respective conditional
probabilities shall be 𝑃 (𝑥𝑖 ∣𝜔1 ) = 𝑝𝑖 and 𝑃 (𝑥𝑖 ∣𝜔2 ) = 𝑞𝑖 , in ourbinary classification case comparing targets vs. non–targets or
ipsilateral vs. contralateral responses. In Bayesian rule, given
the value of x the class membership is decided according to
the probabilities likelihood ratio
𝑃 (𝜔1 )𝑃 (x∣𝜔1 )> (<)1.
𝑃 (𝜔2 )𝑃 (x∣𝜔2 )(2)

The adoption of features independence principle allows us tolimit a number of necessary training samples and we can write
𝑃 (x∣𝜔1 ) =𝑃 (x∣𝜔2 ) =

𝑙
∏
𝑖=1
𝑙
∏
𝑖=1

𝑝𝑥𝑖 𝑖 (1 − 𝑝𝑖 )1−𝑥𝑖(3)𝑞𝑖𝑥𝑖 (1 − 𝑞𝑖 )1−𝑥𝑖(4)

Now an application of a logarithm function to the both sidesof the equation (2) results with a linear discriminant function
as
)
𝑙 (
∑
𝑝𝑖
1 − 𝑝𝑖ℎ(x) =
𝑥𝑖 ln + (1 − 𝑥𝑖 ) ln
(5)𝑞𝑖
1 − 𝑞𝑖𝑖=1+ ln

𝑃 (𝜔1 ),
𝑃 (𝜔2 )

which could be brought to the linear form ofℎ(x) = w𝑇 x + 𝑤0 ,(6)based on the following substitutions[]𝑇𝑝1 (1 − 𝑞1 )𝑝𝑙 (1 − 𝑞𝑙 )w =ln
, . . . , ln
𝑞1 (1 − 𝑝1 )𝑞𝑙 (1 − 𝑝𝑙 )𝑙∑
1 − 𝑝𝑖𝑃 (𝜔1 )
.
𝑤0 =
ln
+ ln
1
−
𝑞
𝑃 (𝜔2 )𝑖𝑖=1
The results of NBC technique successful application arepresented in the next section.
IV. R ESULTS
As the result of the presented research have obtained theresults showing that for the both experimental settings ofsaBCI offline paradigm the classical 𝑃 300 latency could be
improved with the pure 𝑁 200 or the more complex 𝑁 2𝑎𝑝𝑐
features identified with 𝑝–values calculated using the classicalt–test for significance. We summarize below the obtained
results.
A. The Classification Results from the Combined 𝑁 200 and
𝑃 300 ERP Latencies in the Classical target vs. non–target
Setting
The first summary of classification results is presented inTable I, where classification accuracies for the features drawn
from 𝑁 200, 𝑃 300 and the combined latencies are shown.
The majority of the subjects performed already above thechance level of 50% (except subject MA for the pink noisecase) for single feature latencies of 𝑁 200 or 𝑃 300. The
proposed combination of the two “hand–picked” feature setsusing the t–test significant ERP samples allowed us to boost
the classification results up to 7% (only a single case of theaccuracy decrease has been reported) using the leave–one–out
cross validation [13] for the NBC technique.
TABLE IT HE CLASSIFICATION RESULTS FOR ERP LATENCIES IN 𝑁 200 AND
𝑃 300 RESPONSES FOR target VS . non–target PARADIGM . T HE THREEFEATURE SETS (𝑁 200, 𝑃 300 AND 𝑁 200/𝑃 300 LATENCIESCOMBINED ) CLASSIFICATION RESULTS ARE COMPARED . T HE
CLASSIFICATION IMPROVEMENT COMPARING THE CLASSICAL

𝑃 300 LATENCY ONLY WITH THE PROPOSED COMBINATION OF
𝑁 200/𝑃 300) IS SUMMARIZED IN THE RIGHT COLUMN .
noise
N200 P300 N200/P300 N200/P300
subject
stimulus
only
only
combinedvs. P300
type
[%]
[%]
[%]
[%]
pink
63
63
64
1
ZH
white
54
59
60
1
pink
52
54
56
2
TO
white
56
69
68
−1
pink
53
57
57
0
NI
white
57
57
58
1
pink
60
69
69
0
MO
white
55
58
65
7
pink
65
65
67
2
MA
white
46
40
44
4
pink
54
59
59
0
CH
white
53
52
53
1
pink
64
61
66
5
CA
white
53
61
63
2

TABLE II
T HE CLASSIFICATION RESULTS FOR THE PROPOSED METHODUSING 𝑁 2𝑎𝑝𝑐 RESPONSE TO SUPPORT THE SA BCI COMPAREDWITH THE CONVENTIONAL METHOD .
noise
conventional
N2apc
the
subject
stimulus
methodparadigm improvement
type
[%]
[%]
[%]
pink
56
61
5
ZH
white
51
63
12
pink
52
61
9
TO
white
63
67
4
pink
58
58
0
NI
white
47
54
7
pink
37
54
17
MO
white
49
50
1
pink
49
54
5
MA
white
50
56
6
pink
32
48
16
CH
white
48
50
2
pink
72
68
−4
CA
white
58
69
11

B. The Classification Results from the new 𝑁 2𝑎𝑝𝑐 ERP Feature in the Ipsilateral vs. Contralateral Settings
The results of the proposed approach to compare ipsilateraland contralateral to target evoked potentials have been summarized in the Table II, based on the ERP features drawn
from results of the t–test analysis as summarized in the
Figures 4 and 5. The classification accuracy results have been
17% boosted in the best case, with the same method of the
NBC leave–one–out cross validation.
TABLE IIIT HE ITR, SEE EQUATIONS (7) AND (8), FOR THE THREE ERP INTERVALRELATED CLASSIFICATION APPROACHES USING 𝑁 200 OR 𝑃 300 ONLY,AND THE COMBINED 𝑁 200/𝑃 300 TOGETHER .
noise
N200
P300
N200/P300 N200/P300
subject
stimulus
only
only
combinedvs. P300
type[bit/min][bit/min][bit/min][bit/min]
pink
25.84
25.84
26.88
1.04
ZH
white
17.38
21.88
22.84
0.96
pink
15.72
17.38
19.12
1.74
TO
white
19.12
32.40
31.25
−1.15
pink
16.14
20.02
20.02
0.00
NI
white
22.84
20.02
20.94
0.92
pink
22.84
32.40
32.40
0.00
MO
white
18.24
20.94
27.94
7.00
pink
27.94
27.94
30.13
2.19
MA
white
11.19
7.36
9.84
2.48
pink
17.38
21.88
21.88
0.00
CH
white
16.54
15.72
16.54
0.82
pink
26.88
23.82
29.02
5.20
CA
white
16.54
23.82
25.84
2.02

C. 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 [4]:()
1−𝐶
𝑅 = log2 𝑁 + 𝐶 ⋅ log2 𝐶 + (1 − 𝐶) ⋅ log2
, (7)
𝑁 −1
where 𝐶 is the classification accuracy and 𝑁 is the number ofclasses (𝑁 = 6 in this paper). The final bit–per–minute–rateTABLE IV
T HE ITR, SEE EQUATIONS (7) AND (8), FOR THE PROPOSEDMETHOD USING 𝑁 2𝑎𝑝𝑐 RESPONSE TO SUPPORT THE SA BCI
CLASSIFICATION RATES .
noise
conventional proposed
resultingsubject
stimulus
methodN2apcchangetype[bit/min][bit/min][bit/min]
pink
19.12
23.82
4.70
ZH
white
14.92
25.84
10.92
pink
15.72
23.82
8.10
TO
white
25.84
30.13
4.29
pink
20.94
20.94
0.00
NI
white
11.90
17.38
5.48
pink
5.72
17.38
11.66
MO
white
13.37
14.13
0.76
pink
13.37
17.38
4.01
MA
white
14.13
19.12
4.99
pink
3.39
12.62
9.23
CH
white
12.62
14.13
1.51
pink
35.98
31.25
−4.73
CA
white
20.94
32.4
11.46

ERPs at early latencies before 300ms are useful and theyguarantee good classification results and ITRs. These results
reveal that the very early spatial auditory ERPs are potentiallyinteresting for faster BCI applications.
ACKNOWLEDGEMENTS
This research was supported in part by the Strategic Information and Communications R&D Promotion Programme 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.
The first author’s conference trip was supported by Tateisi
Science and Technology Foundation in Japan, grant no.
2022105.
R EFERENCES

𝐵 is obtained after a multiplication by a classification speed
𝑉 , resulting in bits–per–minute [bit/min] as:
𝐵 =𝑉 ⋅𝑅(8)

The ITR results are summarized in Tables III and IV. For theboth cases of the 𝑁 200/𝑃 300 combination and the 𝑁 2𝑎𝑝𝑐
paradigm, there is a significant increase of ITR for the majorityof subjects.
V. C ONCLUSIONSIn this paper we presented two approaches leading to improvements of classification accuracy and ITR in offline saBCI
paradigm by introducing the novel ERP feature extraction
in combined 𝑁 200/𝑃 300 latencies and in the new 𝑁 2𝑎𝑝𝑐
setting which compares responses of lateral, to the head, soundsources.
The first improvement analysis resulted in a comparison ofclassification rates for the three ERP feature sets of 𝑁 200
and 𝑃 300 latencies processed separately, versus the combined
𝑁 200/𝑃 300. The latter combination resulted in a steadyincrease in classification accuracy for the majority of subjects
up to 7% at maximum. Additionally the ITR improvement inthis case was reported at maximum of 7bit/min. This is a very
good result giving a possibility to further improve the auditory
paradigm based BCI.
The second improvement step is based on the proposed
extension of 𝑁 2𝑎𝑐 concept. We added a comparison of parietal
electrodes responses allowing for the new feature creationfrom such ERP comparisons. The new ERP component wasnamed 𝑁 2𝑎𝑝𝑐 since it combines anterior and posterior contralateral response differences. The obtained classification and
ITR improvement was also very encouraging.
The two main achievements reported in the paper allowed us
to improve the novel saBCI paradigm in offline mode which isa step forward in the non–vision based interfacing strategies.
The obtained results reveal that not only the cortical auditory
information processing centers related to the cognitive streams
could be utilized to BCI purposes. Also the differences in[1] J. Wolpaw and E. W. Wolpaw, eds., Brain-Computer Interfaces: Principles and Practice. Oxford University Press, 2012.
[2] J. Schnupp, I. Nelken, and A. King, Auditory Neuroscience - Making
Sense of Sound. MIT Press, 2010.
[3] T. M. Rutkowski, A. Cichocki, and D. P. Mandic, “Spatial auditory
paradigms for brain computer/machine interfacing,” in International
Workshop On The Principles and Applications of Spatial Hearing 2009
(IWPASH 2009) - Proceedings of the International Workshop, (MiyagiZao Royal Hotel, Sendai, Japan), p. P5, November 11-13, 2009.
[4] M. Schreuder, B. Blankertz, and M. Tangermann, “A new auditory
multi-class brain-computer interface paradigm: Spatial hearing as an
informative cue,” PLoS ONE, vol. 5, p. e9813, 04 2010.
[5] M. L. Gamble and S. J. Luck, “N2ac: An ERP component associatedwith the focusing of attention within an auditory scene,” Psychophysiology, vol. 48, pp. 1057–1068, 2011.
[6] Z. Cai, H. Terasawa, S. Makino, T. Yamada, and T. M. Rutkowski,“Sound timbre and spatial location as informative cues in auditory BCI- brain evoked potential enhancement and involuntary eye movements
artifacts suppression approach,” in Proceedings of the Third APSIPA
Annual Summit and Conference (APSIPA ASC 2011), (Xi’an, China),
p. paper 241 (6 pages), APSIPA, October 18 - 21, 2011.
[7] E. Niedermeyer and F. L. Da Silva, eds., Electroencephalography:
Basic Principles, Clinical Applications, and Related Fields. Lippincott
Williams & Wilkins, 5 ed., 2004.
[8] M. Schreuder, T. Rost, and M. Tangermann, “Listen, you are writing!
Speeding up online spelling with a dynamic auditory BCI,” Frontiers in
Neuroscience, vol. 5, p. Article 112, 2011.
[9] J. Hoehne, M. Schreuder, B. Blankertz, and M. Tangermann, “A novel
9-class auditory ERP paradigm driving a predictive text entry system,”
Frontiers in Neuroscience, vol. 5, p. Article 99, 2011.
[10] T. M. Rutkowski, “Auditory brain-computer/machine interface
paradigms design,” in Haptic and Audio Interaction Design (E. Cooper,
V. Kryssanov, H. Ogawa, and S. Brewster, eds.), vol. 6851 of Lecture
Notes in Computer Science, pp. 110–119, Springer Berlin / Heidelberg,
2011.
[11] V. Jurcak, D. Tsuzuki, and I. Dan, “10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems,”
NeuroImage, vol. 34, no. 4, pp. 1600 – 1611, 2007.
[12] H. F. van Emden, Statistics for Terrified Biologists. Blackwell Publishing, 2008.
[13] S. Theodoridis and K. Koutroumbas, Pattern Recognition. Acedemic
Press, fourh ed., 2009.

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