Cogn Comput
DOI 10.1007/s12559-013-9228-x

Brain Evoked Potential Latencies Optimizationfor Spatial Auditory Brain–Computer Interface
Zhenyu Cai • Shoji Makino • Tomasz M. Rutkowski

Received: 11 December 2012 / Accepted: 25 July 2013
Ó Springer Science+Business Media New York 2013

Abstract We propose a novel method for the extractionof discriminative features in electroencephalography(EEG) evoked potential latency. Based on our offline
results, we present evidence indicating that a full surroundsound auditory brain–computer interface (BCI) paradigm
has potential for an online application. The auditory spatial
BCI concept is based on an eight-directional audio stimuli
delivery technique, developed by our group, which
employs a loudspeaker array in an octagonal horizontal
plane. The stimuli presented to the subjects vary in frequency and timbre. To capture brain responses, we utilize
an eight-channel EEG system. We propose a methodology
for finding and optimizing evoked response latencies in the
P300 range in order later to classify them correctly and to
elucidate the subject’s chosen targets or ignored non-targets. To accomplish the above, we propose an approachbased on an analysis of variance for feature selection.
Finally, we identify the subjects’ intended commands witha Naive Bayesian classifier for sorting the final responses.
The results obtained with ten subjects in offline BCI
experiments support our research hypothesis by providing
higher classification results and an improved informationtransfer rate compared with state-of-the-art solutions.

Z. Cai
Graduate School of Systems and Information Engineering,
University of Tsukuba, Tsukuba, JapanS. Makino  T. M. Rutkowski (&)
Tsukuba Advanced Research Alliance (TARA) Center,
University of Tsukuba, Tsukuba, Japane-mail: tomek@tara.tsukuba.ac.jp
T. M. Rutkowski
RIKEN Brain Science Institute, Wako-shi, Japan

Keywords Auditory BCI  Spatial auditory
cognition  EEG  Event-related potential (ERP) 
Feature extraction  Statistical signal processing

Introduction
A brain–computer interface (BCI) is designed to establish acommunication link between the human brain and a computer [1]. A BCI does not depend on muscle or peripheral
nervous system activity. In particular, a BCI could help
patients suffering from amyotrophic lateral sclerosis (ALS)
to communicate or to complete various daily tasks,
including controlling a computer or typing messages on a
virtual keyboard. In recent years, auditory BCI (aBCI) has
become a topic of great interest in computational auditory
neuroscience. The aBCI utilizes human auditory pathway
responses and allows users to operate external devices
more quickly and simply, based on auditory evoked
responses to sound stimuli. We utilize a spatial auditory
paradigm to create a new aBCI system with which users
consciously direct their attention to different locations in ahorizontal surround sound environment with various noise
stimuli, as depicted in Fig. 1. The spatial aBCI concept isfounded on a basic feature of the human auditory pathway,
which is very sensitive to the location of changing spatial
auditory sources [2]. The auditory pathway also has a very
good temporal resolution, which is an additional feature wewould like to utilize in the spatial aBCI design. This will
make it possible to reduce inter-stimuli intervals (ISI) of
the presented sounds in comparison with vision-based
applications [3]. Contemporary applications have thus far
failed to use rear-to-the-head loudspeakers, as postulated as
an optimal setting yet still not fully realized in Schreuder
et al. [4]. Our proposal includes sound presentation from123

Cogn Comput

rear loudspeakers, thus realizing an eight-command BCI(octagonal horizontal plane full surround sound set-up). In
previous publications, we discussed EEG electrodes
selection, event-related potential (ERP) features optimization and linear discriminative analysis classification [5].
These earlier results were unsatisfactory as regards a successful online aBCI system application.
In this paper, we discuss a novel aBCI paradigm basedon the full surround sound horizontal stimuli as an informative cue with the support of the P300 component(known as the ‘‘aha response’’) at a latency around andafter the 300 ms, evoked by the expected/instructed targets[6]. Our hypothesis is that a significant ERP response will
be found when subjects attend to the target direction andignore the non-targets. To find the significant differences,
we propose analyzing the response statistically to identify
only those ERP latencies that contribute to the classification enhancement, in contrast to state-of-the-art approaches[1], in which the whole response is taken as a feature forsubsequent classification.
The hypothesis of our research is that the horizontal fullsurround sound aBCI paradigm could be improved with thecareful selection of ERP discriminative features that allows
the use of the rear-to-the-head sound directions. For thispurpose, we introduce a statistical response analysis, which
ultimately leads to the final improvement in the information transfer rate (ITR).

Fig. 1 Spatial auditory BCI paradigm concept with eight loudspeakers in the upper part of the figure. The lower graph visualizes thestimulus presentation concept in the time domain. Each stimulus ispresented for 30 ms with 170-ms silent breaks, so the ISI is set to
200 ms123

The paper is organized as follows. In the next section,
the experimental paradigm is explained together with the
EEG preprocessing steps. Then, we discuss EEG feature
selection using the method of statistical analysis of ERP
responses. Finally, we present classification results
obtained with a Gaussian Naive Bayesian Classifier(GNBC), which leads to an improvement in the ITR scores.
The final section summarizes the paper.

Methods
Within the framework of the proposed novel aBCI paradigm, the subjects were asked to attend to and count targets
while ignoring non-targets, as in the classical oddballparadigm [1, 6]. A target direction instruction regarding
which direction should be attended to in each trial was
displayed visually on a computer display located in front of
the subject. First, we conducted psychophysical experiments to check possible preferred directions of the subjects
by comparing response time delays. Next we conducted
EEG recording experiments in an offline BCI setting. The
EEG signals were recorded with a g.MOBILab? EEG
amplifier by g.tec. We used novel dry EEG electrodes
g.SAHARA to further improve the subjects’ comfort, sincethese do not require conductive gel. The reference andground electrodes were attached behind the left and rightears, respectively. To reduce unnecessary noise and toprevent degradation of the EEG signal quality as a result ofelectromyography (EMG) noise related to muscular
movement in the ERP responses, the subjects were asked tominimize the blinking of their eyes, and facial and body
movements during the experiments.
EEG experiments designed to validate the proposed
spatial aBCI paradigm utilizing the P300 latency wereconducted in the Multimedia Lab at the Life Science
Center of TARA, University of Tsukuba, Japan. All theexperimental procedures and study targets were explained
to the subjects, who agreed to participate voluntarily. The
experiments were conducted in agreement with the WMA
Declaration of Helsinki—Ethical Principles for Medical
Research Involving Human Subjects. All the experiments
were conducted in a silent and low reverberation room inorder to limit any interference from environmental acoustic
noise.
The auditory stimuli were presented through eightloudspeakers in an octagonal setting, as depicted in theupper part of the Fig. 1. The eight sound stimuli directions
proved to be optimal from the points of view of aBCI andhuman subject spatial auditory performance [4].
Two short white and pink noise stimuli bursts were usedas depicted in the lower part of the Fig. 1 and described in
the following section.

Cogn Comput

Psychophysical Experiment
In the psychophysical experiment, only the behavioral
responses (button presses after the instructed and perceived
target stimuli) were recorded. Different response timedelays would suggest changing cognitive loads and taskdifficulties in function of the various spatial directions. The
subjects were requested to press a button immediately after
an instructed target direction was presented. The responsedelays in respect of auditory stimuli onsets were recorded
and further analyzed in order to compare them with various
spatial directions.
The results of the psychophysical experiment are presented in Tables 1 and 2. As a result of the tests conducted,
we conclude that all the eight spatial sound stimuli locations had the same (differences among means statistically
non-significant when compared with pairwise t tests) values for all the tested octagonal stimulus spatial directions
for white and pink noise, which also confirms psychophysical experiments reported in Schreuder et al. [4].
The Offline aBCI Experiment Protocol
The experimental hypothesis was that we would be able todistinguish from the ERP shape (mainly based on the P300
response latencies) which direction the subject attended toin the spatial auditory paradigm experiment.
To test the hypothesis, we conducted a series of EEG
recording experiments in the offline BCI mode (with no
instant feedback or classification results given to the subjects [1]). EEG recording experiments were conducted withthe ten healthy subjects (eight males; two females; age
range from 23 to 42 years, mean 25.8, SD 6.34). The
subjects were requested to sit in a comfortable chair in thecenter of eight octagonally positioned loudspeakers, andthe dry EEG electrodes were positioned on the scalp. The

elevation of the loudspeakers was fixed at the subject’s ear
level in order to create a horizontal spatial plane defined by
the eight loudspeakers (see Fig. 1). The volume of thesound was set to 72 dB.
The sound stimuli were presented in random order andone at a time from a single loudspeaker (a single trial
consisted of a delivery of a single target and seven nontargets). We employed two broadband noise stimuli types
that allowed us to utilize the two spatial localization
mechanisms of the human auditory pathway, the interaural time delay (ITD) and the interaural level difference(ILD) [2]. The white and pink noise stimuli both had
30 ms lengths with 5-ms linear attack and sustain intervals. For each subject and each stimulus, we performed
eight sessions (altogether 64 targets and 448 non-targets
were presented). Each subject was requested to focus on
the instructed target direction which was presented on acomputer display. The subject ignored the other nontarget directions. Each subject was also requested tocontrol her/his eye movements to decrease the unnecessary EMG noise during the experiments. Before eachexperiment, the subject was allowed a short practicesession to get familiar with the spatial auditory stimulus
conditions.
EEG Acquisition
The EEG signals were recorded by the g.MOBILab? bioamplifier with eight dry g.SAHARA electrodes. The EEG
recording system captured the neurophysiological signals
in a frequency range of 0.1–40.0 Hz. The following eight
EEG electrode positions were chosen P3, P4, P5, P6, Cz,
CPz, Pz, and POz, as in the 10/10 system [7]. The eight
EEG channels were sampled with 256 Hz frequency andstored using a custom application programmed in MATLAB
and Simulink environments.

Table 1 The spatial sound psychophysical experiment results
Front left

Front right

Rear left

Rear right

Front

Rear

Left

Right

Grand mean of delay time (ms)

455

474

447

490

450

464

466

462

STD of response time delay (ms)

63

76

42

75

59

76

73

65

Accuracy (%)

100

100

72

72

94

78

94

83

0

0

33

25

14

27

14

28

Grand mean of delay time (ms)

439

458

448

478

445

489

450

477

STD of response time delay (ms)

63

48

65

52

68

59

56

55

Accuracy (%)

100

94

83

83

94

94

94

89

0

0

33

25

14

27

14

28

Pink noise stimulus

STD of accuracy (%)
White noise stimulus

STD of accuracy (%)

The response time delays and instructed directions accuracies are presented in the form of mean values with standard deviations (STD),
respectively

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Cogn Comput
Table 2 The confusion matrix results from the psychophysical experiment averaged for all the subjects for pink and white noise stimulus,
respectively
Accuracy (%)

Front left

Front right

Rear left

Rear right

Front

Rear

Left

Right

Confusion matrix in psychophysical tests using pink noise stimulus
Front left

100

0

0

0

0

0

0

0

Front right

0

100

0

0

0

0

0

0

Rear left

6

6

72

5

0

0

5

0

Rear right

5

0

6

72

0

6

0

6

Front

0

0

5

0

78

11

0

6

Rear
Left

0
0

0
0

5
6

0
0

6
0

78
0

0
94

6
0

Confusion matrix in psychophysical tests using white noise stimulus
Right

0

0

0

6

0

11

0

83

Front left

100

0

0

0

0

0

0

0

Front right

0

94

0

6

0

0

0

0

Rear left

0

0

83

5

0

0

0

6

Rear right

0

6

0

83

5

0

0

6

Front

0

0

0

6

94

0

0

0

Rear

0

0

6

0

0

94

0

0

Left

0

0

0

6

0

0

94

0

Right

0

0

0

6

0

11

0

83

EEG Response Analysis
The analysis of EEG ERP responses, leading to the final
eight-direction spatial auditory classification for target andnon-target locations, was composed of the following threesteps:
1.
2.
3.

EEG signals preprocessing: band-pass filtering, epoch
segmentation, and artifact rejection;
Discriminative feature extraction using the analysis ofvariance (ANOVA) method;
The final classification of evoked response using the
GNBC.

100 ms prestimuli onset interval as the baseline (seeFig. 2).
In the next step, the rejection of eye movement artifacts
was carried out. Auditory spatial stimuli are known tocause uncontrolled eye movements in subjects [8], which inthe current approach were removed with a threshold value
set at the 80 lV (signal amplitude level above the usual
EEG activity). The rejected epochs were not further processed, since in the current approach, the emphasis was on
the spatial paradigm validation. In the following sections,feature extraction and ERP classification results are
introduced.

We describe the above steps in detail in the following
sections.

ERP Feature Extraction Using ANOVA of the ERP
Latencies

EEG Preprocessing

The aim here was to optimize the EEG response domain(mainly P300 response), which would provide a better
separability for further classification. In order to do this, we
conducted ANOVA of the two-class single-trial ERP distributions (target vs. non-target responses) in the spatial
auditory experimental setting. The ERP response distributions passed ‘‘normality tests’’ and were comparable tomore flexible methods such as the area under the curve
analysis, yet the proposed ANOVA yielded the best resultsin our case. The majority of spatial aBCI applications aim
at the P300 response latency [4, 5, 9, 10]. The example inFig. 2 shows the averaged ERP responses to targets andFirst, we filtered digitally the signals with the two fifthorder Butterworth high- and low-pass filters, which wereapplied with cut-off frequencies at 0.5 and 25 Hz. The lowpass filtering removed possible muscle-activity-related
artifacts. The high-pass filtering removed the direct current-related drifts of the EEG signals, as well as slow eye
movement artifacts.
Next, the EEG signals were segmented creating the
ERP-related epochs. Each epoch started 100 ms before
stimulus onset and it ended after 700 ms. We used the123Cogn Comput
grand mean ERPs (pink noise)target3

Channel [µV]

2.5

1.5
1
0.5

4
2
0
−2
0

100

−0.5
−1
−1.5
−100

0

100

200

300

400

500

600

700

time (in ms after each stimulus onset)
grand mean ERPs (white noise)

CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

700

−4

4

0
−2
0

100

300

500

700

−4

time [ms]p−value

2.5

Channel2

EEG amplitude [uV]

500

2

3

1.5
1
0.5
0

CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

0.6
0.5
0.4
0.3
0.2
0

100

300

500

700

time [ms]

−0.5
−1
−1.5
−2
−100

300

time [ms]non−target

0

Channel [µV]

EEG amplitude [uV]

2

CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

0

100

200

300

400

500

600

700

time (in ms after each stimulus onset)
Fig. 2 The grand mean averaged ERP responses of the ten subjects.
Results of ERP P300 response for pink noise (upper panel) and white
noise (lower panel). The solid red lines depict the attended targets
and the blue dashes the ignored non-targets. The differences between
targets and non-targets are very easy to distinguish after 300 ms (theso-called aha or P300 response) (Color figure online)

non-targets (note the latencies range 300–600 ms). Next,
the ANOVA method was applied to compare the differences of response distributions in single trials for eachsample point of the collected ERPs. As a result, we wereable to extract discriminative information leading to later
classification optimization. The results of the above analysis are depicted in Figs. 3 and 4. The bottom panels in theabove figures visualize the ANOVA’s p values for eight
electrodes separately in each row using a color scheme, as
explained in the color bars next to the panels. The p values
are the probabilities of the null hypotheses that the distributions are significantly different (in life sciences, usually

Fig. 3 Grand mean average auditory evoked responses to spatial
white noise stimuli of the ten subjects from the eight electrodes
plotted separately in each row of the panels. The top panel shows the
grand mean averaged response to the targets. The middle panelpresents the grand mean averaged responses to non-targets. The
bottom panel depicts the p values from the ANOVA for the eight
electrodes separately

p \ 0.05 is considered to be a significant value). The
results in Figs. 3 and 4 clearly show that the postulated
P300 latency area in the range 300–600 ms is the best to
discriminate attended targets from ignored non-targets.
This finding confirms our hypothesis that the P300 latencies are also related to spatial cognition in the human brain.
Next in this paper, the binary classification problem is
discussed. We evaluate our hypothesis that the ‘‘handpicked’’ P300 latency ERP periods are significant features
to improve the binary target vs. non-target classification
accuracy. In order to find the most discriminable featuresfrom ERP responses, we used the results from the ANOVA
method described above applied to the all ERP latencies.
We ‘‘hand-picked’’ only those samples within each subject’s ERPs for which the p values were smaller than 0.05
(as depicted in blue in Figs. 3 and 4) in the range
300–600 ms.

123Cogn Comput

Channel [µV]target
CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

4
2
0
−2
100

300

500

700

−4

to more sophisticated classification methods [12] for particular cases as reported in this paper. In our approach, weutilized a NaN-Toolbox which is a part of a BioSig environment [13]. The classifier input features were the real
micro-volt EEG ERP latency values ‘‘hand-picked’’ asdiscussed in the previous section.
The results of the successful application of the GNBC
technique are presented in the next section.

time [ms]Channel [µV]

non−target
CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

4

0
−2
100

300

500

700

−4

Channeltime [ms]p−value
CPz
Cz
P3
P4
P5
P6
POz
Pz
−100

The proposed approach resulted in the improvement in theaBCI paradigm for setting of both the combined andaveraged electrodes for each subject separately. Detailed
results are presented in the following sections. First, we
introduce the ITR, which is a commonly used measure tocompare various paradigms in the BCI research community[4]. We also present classification accuracy results.

0.8
0.6
0.4
0.2
100

300

500

700

time [ms]Fig. 4 Grand mean average auditory evoked responses to spatial pinknoise stimuli of the ten subjects from the eight electrodes plotted
separately in each row of the panels. The top panel shows the grandmean averaged response to the targets. The middle panel presents the
grand mean averaged responses to non-targets. The bottom panel
depicts the p values from the ANOVA for the eight electrodes
separately

The Offline ERP Classification in the aBCI Paradigm
We performed the classification steps for each subject
separately in aBCI offline mode, which means that all theprocedures were conducted after the collection of data fromeach experiment, without any online feedback to the subjects. The classification procedure in our case is a so-calledbinary task paradigm (target vs. non-target). In the classifier training and testing step, we selected 64 targets and arandom subset of 64 non-targets (from the 448 available)
to have a balanced number of the members in each classset. The resulting theoretical chance level was thus 50 %.
Based on our previous classification trails reported in Cai
et al. [5, 11], we proposed to use a Bayesian classifier,which yielded similar or even better results on our experimental data than linear discrimination analysis methods.
The GNBC is particularly suited to highly dimensionalfeatures. The GNBC method produced results comparable

123

Results2

Analysis of aBCI Results with ITR and Classification
Accuracies
The amount of information carried by each selection in the
BCI application is usually quantified by the ITR, which is
calculated based on bit-per-selection R, defined as [4]:1CR ¼ log2 N þ C  log2 C þ ð1  CÞ  log2
;
ð1Þ
N1
where C is the classification accuracy and N is the numberof classes (N = 8 in this paper). The final ITR is obtained
after a multiplication by a classification speed V, resulting
in a bit-per-minute rate (bit/min) as:
ITR ¼ V  R

ð2Þ

The ITR results are summarized in Tables 3 and 5 anddiscussed in detail in the following sections. The GNBC
classification accuracies are also summarized in Tables 4
and 6.
The ITR and Classification Accuracy Resultsfrom the P300 ERP Range Latencies in the Single
Channel Setting of Target Versus Non-target
A summary of the ITR results is presented in Table 3 (thecorresponding classification accuracy is summarized inTable 4). We compare the ITR values obtained for thefeatures drawn from the conventional whole ERP and the
proposed ‘‘hand-picked’’ features resulting from the
ANOVA. The proposed method allows us to boost theclassification results up to ?10.43 bit/min (?44 % classification accuracy). Only a single case shows a decreaseCogn Comput
Table 3 The offline aBCI
interfacing results based onfeatures drawn from nonaveraged trials in the form of
ITR scores obtained as in Eqs.
(1) and (2)Subject

Noise stimulus
type

Conventional ‘‘all ERP’’(bit/min)

Proposed ‘‘hand-picked’’(bit/min)

Improvement(bit/min)

#1

Pink

49.39

54.13

?4.74

White

37.90

57.42

?19.52

#2

Pink

42.03

49.39

?7.36

White

27.90

44.90

?17

#3

Pink

35.26

39.25

?3.99

White

32.72

42.03

?9.31

#4

Pink

27.90

40.63

?12.73

White

19.30

29.07

?9.77

Pink

47.86

66.19

?18.33

White

47.86

57.42

?9.56

#6

Pink
White

49.39
36.57

46.37
71.87

-3.02
?35.30

#7

Pink

46.37

54.13

?7.76

White

47.86

57.42

?9.56

#8

Pink

49.39

68.05

?18.66

White

42.03

43.45

?1.42

#9

Pink

50.94

50.94

0

White

54.13

75.85

?21.72#5

We compare the traditional all
ERP and the proposed ‘‘handpicked’’ only latencies

Table 4 The classification
results for ERP latencies in
P300 responses for target versus
non-target paradigm

The classification results of two
feature sets (all ERP responses
and P300 responses) arecompared. The classification
improvement comparing theconventional all ERP latency
with the proposed P300
response is summarized in theright column

#10

Pink

39.25

37.90

-1.35

White

44.90

49.39

?4.49

Subject

Noise stimulus
type

Conventional‘‘all ERP’’ (%)

Proposed
P300 (%)

Improvement
(%)

#1

Pink

71

74

?3

White

63

76

?13

#2

Pink

66

71

?5

White

55

68

?13

#3

Pink

61

64

?3

White

59

66

?7

#4

Pink

55

65

?10

White

47

56

?9

#5

Pink

70

81

?11

#6

White
Pink

70
71

76
69

?6
-2

White

62

84

?22

#7

Pink

69

74

?5

White

70

76

?6

#8

Pink

71

82

?11

White

66

67

?1

#9
#10

Pink

72

72

0

White

74

86

?12

Pink

64

63

-1

White

68

71

?3

123

Cogn Comput
Table 5 The offline aBCI
interfacing results based onfeatures drawn from theaveraged eight trials in the formof ITR scores obtained as in
Eqs. (1) and (2)Subject

Noise stimulus
type

Conventional ‘‘all ERP’’(bit/min)

Proposed ‘‘hand-picked’’(bit/min)

Improvement(bit/min)

#1

Pink

8.27

14.06

?5.79

White

3.63

6.97

?3.34

#2

Pink

6.97

14.06

?7.09

White

4.74

8.27

?3.53

#3

Pink

5.80

10.00

?4.20

White

5.80

5.80

0

#4

Pink

4.74

10.00

?5.26

White

2.80

4.74

?1.94
?2.47

#5

We compare the traditional
whole ERP and the proposed‘‘hand-picked’’ only latencies

Table 6 The classification
results for ERP latencies in
P300 responses for the mean of
8 targets versus average of 8
non-targets paradigm

Pink

5.80

8.27

White

10.00

11.74

?1.74

#6

Pink
White

6.97
5.80

10.00
10.00

?3.03
?4.20

#7

Pink

3.63

14.06

?10.43

White

6.97

8.27

?1.30

#8

Pink

6.97

10.00

?3.03

White

4.74

11.74

?7.00

#9

Pink

5.8

11.74

?5.94

White

6.97

11.74

?4.77

#10

10.00

?3.03

11.74

?4.77

Noise
stimulus type

Conventional‘‘all ERP’’ (%)

Proposed
P300 (%)

#1

Pink

81

100

?19

White

56

75

?19

Pink

75

100

?25

White

63

81

?18

Pink

69

88

?19

#3

123

6.97
6.97

Subject#2

The classification results for two
feature sets (all ERP responses
and P300 responses) arecompared. The classification
improvement comparing theconventional all ERP latency
with the proposed P300
response is summarized in theright column

Pink
White

Improvement
(%)

White

69

69

0

#4

Pink

63

88

?25

White

50

63

?13

#5

Pink

69

81

?12

#6

White
Pink

88
75

94
88

?6
?13

White

69

88

?19

#7

Pink

56

100

?44

White

75

81

?6

#8

Pink

75

88

?13

White

63

94

?31

#9

Pink

69

94

?25

White

75

94

?19

#10

Pink

75

88

?13

White

75

94

?19

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using the leave-one-out cross-validation for the GNBC
technique.
The ITR Results from the P300 ERP Range Latencies
from the Averaged Eight Trials in the Setting of Target
Versus Non-target
The single-trial classification results discussed in the previous sections have resulted mostly in lower than 14 bit/min (below 70 % accuracy) in the aBCI offline mode. In
order to improve the results, for each subject and eachstimuli, we averaged the eight target trails (convert 64
targets to 8 targets) and 8 non-target trials (convert 448
non-targets to 56 non-targets). In the classifier training andtesting steps, we selected 8 targets and a random subset of
8 non-targets (from the 56 available), the same as the
single-trial classification training and testing set. For eachof the subjects in the case of the pink noise stimulus, theclassification resulted in scores higher than 8 bit/min(80 % accuracy). In particular, for the subject numbers 1
and 2, the resulting ITR reached 14.06 bit/min (100 %accuracy). The summary of the results is presented inTables 5 and 6 for ITR and classification accuracies,
respectively. The comparison presented of the ITR andaccuracy values obtained for the features drawn from theconventional whole ERP and the proposed ‘‘hand-picked’’
features, resulting from the ANOVA, supports the
improvement of the proposed method. The method proposed allows us to increase the classification results (only asingle case of a decrease was reported) using the leaveone-out cross-validation for the GNBC technique. An
online aBCI application is planned as a next stage by theauthors.

Discussion and Conclusions
In this paper, we have presented an approach leading to theimprovement of classification accuracies and ITRs in anovel offline aBCI paradigm. This has been achieved byintroducing ERP feature extraction in P300 range latencies
to replace the classical whole evoked response range
approaches.
The proposed improvement method allows the extraction of the most separable ERP features, enabling an
increase in the classification accuracy and an improvement
ITR of a maximum of ?35.30 bit/min (22 % accuracy) in
the case of features drawn for single-electrode ERP distributions. In the case of the features obtained from theeight trials averaged ERP responses, the majority of subjects also improved their results with a maximum increaseof 10.43 bit/min (44 % in accuracy).

These are the very encouraging results, providing thepossibility further to improve the auditory-paradigm-based
BCI.
The main achievement reported in the paper allows us to
improve the spatial aBCI paradigm in the offline mode,
which is a step forward in non-vision-based interfacing
strategies. We have also shown that in comparison with
contemporary applications of spatial auditory BCI paradigms that fail to utilize rear-to-the-head loudspeakers, it ispossible to utilize all spatial horizontal sound directions
thanks to the proposed classification improvement
approach based on the ‘‘hand-picked’’ ERP latencies.
Acknowledgments This research was supported in part by theStrategic Information and Communications R&D Promotion Program
No. 121803027 of The Ministry of Internal Affairs and Communication in Japan and by KAKENHI, the Japan Society for the Promotion of Science, Grant Nos. 12010738 and 24700154. We also
acknowledge the technical support of YAMAHA Sound & IT
Development Division in Hamamatsu, Japan.
Author Contributions Zhenyu Cai, Tomasz M. Rutkowski: Performed the EEG experiments and analyzed the data. Tomasz M. Rutkowski: Conceived the concept of the spatial auditory BCI and designed
the EEG experiments. Shoji Makino: Coordinated and supported the
project. Zhenyu Cai, Tomasz M. Rutkowski: Wrote the paper.

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