Full–body Tactile P300–based Brain–computer
Interface Accuracy Refinement
Takumi Kodama, Kensuke Shimizu, Shoji Makino

Tomasz M. Rutkowski1†‡?

Life Science Center of TARA
University of Tsukuba
Tsukuba, Japan‡ Saitama Institute of Technology, Fukaya, Japan† The University of Tokyo, Tokyo, Japan? RIKEN Brain Science Institute, Wako-shi, Japan

tomek@bci-lab.info
http://bci-lab.info/
Abstract— In this study we report results of a classification accuracy performance comparison for a novel full–body tactile P300–based brain–computer interface (BCI)
paradigm. In the discussed BCI experiments six spatial
vibrotactile stimulus patterns are given to the user bodyentire back and limbs. The acquired somatosensory ERP
signals are classified for the BCI purposes using a step–
wise linear discriminant analysis (SWLDA), linear and
non–linear (Gaussian kernel) support vector machineswith features drown from an electroencephalogram (EEG)
preprocessed signals. The aim of the current project is todetermine the most suitable classification methods for atactile P300–based BCI paradigm and to affirm a validityof vibrotactile stimulus patterns for the user full–body–
based interfacing paradigm. The best grand mean averaged accuracies for each classification method result in
rates of 57.48% for SWLDA, 58.5% for linear and 59.83%
for non–linear SVMs, respectively.
Keywords— Brain–computer interface (BCI); EEG; tactile P300–based BCI; machine learning.
I. INTRODUCTION
A brain–computer interface (BCI) is a neurotechnologythat enables users to express their intention by only using theirbrainwaves [1]. For this reason, in the past decade, several
BCI modalities have been eagerly developed in order to communicate with amyotrophic lateral sclerosis (ALS) patients,
who have difficulty moving their muscles due to neuro–motor
disabilities [2]. A P300 response–based oddball paradigm,
which employs of user mental attention modulation, is oneof the major modality in the BCI study [1, 3]. The studiesof P300 response–based visual and auditory BCI paradigms
have been widely investigated [4, 5].
In this study, we examine a novel P300 response–based
BCI paradigm using a touch sensation, in other words, a tactile BCI [6]. The tactile BCI could be applicable to locked–in
syndrome (LIS) patients who lose their sight and hearing asa late symptom of the ALS disease [7]. Therefore, an establishment of this alternative paradigm shall provide not only abetter communication method for care takers, but also a patient quality of life. Recent tactile BCI studies have reported
practical feasibility of a tactile stimulus for creating an alternative P300–based BCI paradigm [8].
So far, however, there has been little discussion about classification accuracies of the tactile P300–based BCI. Generally, the tactile BCI paradigms have not been considered as
1 The corresponding author.

Figure 1: The fbBCI user lying down on a mattress in which
the vibrotactile transducers (shown in the left lower panel)
were embedded. Six vibrotactile stimulus patterns were given
to the user full–body throughout the fbBCI experiment. The
picture is included with the photographed user permission.

high classification accuracy generating solutions comparing
to the competitive visual or auditory BCI paradigms. Hence,
the more investigation is necessary to acquire superior classification results for a wider usage of tactile P300–based BCI
paradigms.
The main objective in the presented study is to investigate
the most suitable classification method for a tactile P300–
based BCI paradigm in terms of stimulus pattern classification accuracies. We propose a novel P300–based full–bodytactile BCI paradigm (fbBCI) as already tested in a previous
pilot study [9]. The fbBCI applied spatial vibrotactile stimulus patterns to the user’s entire back and limbs in order to
evoke the somatosensory P300 responses. The vibrotactile
transducers were placed with larger distances on a mattressin order to give tactile stimulus patterns to the user. The fbBCI was designed for a practical application for bedridden
patients so the user could test it with their body lying downon the mattress. The previously reported [9] mean tactile BCI
accuracy was of 53.67%. In the current study we aim at improvement of that result.
In the current study, a step–wise linear discriminant analysis (SWLDA), linear and non–linear (Gaussian kernel) SVM
algorithms are tested on the acquired EEG data in the fbBCI paradigm. The EEG signal preprocessing steps include
bandpass filtering, signal decimation and averaging. We test
several combinations of the above mentioned signal preprocessing steps in order to asses BCI classification methods.
Consequently, a potential validity of the proposed full–bodytactile P300–based BCI paradigm modality is finally reconfirmed and discussed in the paper.

Table 1: Conditions of the EEG experiments
Condition
Number of users
Users mean age
EEG recording system
EEG electrode positions
EEG sampling rate
Stimulus generators
Stimulus frequency
EEG acquisition environment
Target stimulus lengthInter–stimulus interval (ISI)
ERP interval

Detail10 (5 males and 5 females)
21.9 years oldg.USBamp active electrodes
EEG system
Cz, Pz, P3, P4, C3, C4, CP5,
and CP6
512 HzDayton Audio TT25-16 transducers
40 Hz
BCI2000
100 ms400 ⇠ 430 ms
0 ⇠ 800 ms after stimulus onsets

II. METHODS
The fbBCI EEG experiment was conducted with ten BCI
naive users (five males and females) with a mean age of 21.9
years old (standard deviation of 1.45 years). All the experiments were executed in the Life Science Center of TARA,
University of Tsukuba, Japan with guidelines and permissionof the institutional ethical committee, as well as in accordancewith The World Medical Association Declaration of Helsinki- Ethical Principles for Medical Research Involving Human
Subjects. All the participating users were paid for their contribution and provided written informed consents.
During the experiment, a user laid down on a Japanese–style mattress containing a polyester filling. The user was
instructed to distinguish six fbBCI stimulus patterns delivered to arms, shoulder, waist and legs, as depicted in Figure 1. The stimulus patterns were created by eight vibrotactile transducers (Dayton Audio TT25–16 as depicted in alower left panel of Figure 1). The stimulus carrier frequenciesof the transducers were set at 40 Hz. A bio–signal amplifiersystem g.USBamp from g.tec Medical Engineering GmbH,
Austria, was employed to record the EEG signals. Following
the 10/10 extended international system, active g.LADYbird
electrodes were attached to Cz, Pz, P3, P4, C3, C4, CP5 andCP6 to head locations to cover the primary somatosensory
and parietal cortices. A reference electrode was attached to
the left earlobe, and a ground electrode to the head FPz position. The EEG recording sampling frequency was set at
512 Hz. The amplifier high– and low–pass filters were set at
0.1 Hz and 60 Hz, respectively. A notch filter was set in a
rejection band of 48 to 52 Hz in order to remove power line
interferences. The EEG signals were captured by BCI2000
acquisition software. Details of the fbBCI EEG experimental
protocol are summarized in Table 1.
In each fbBCI single experimental session 10 targets and
50 non–targets stimulus patterns were randomly presented tothe users. The sessions were repeated until each of the six
stimulus pattern became targets, namely 60 targets and 300
non–targets were presented overall in a single experimental
trial. Each user participated in five trials in a row and thestimulus pattern classification accuracies were calculated byaveraging all of the five trials. The vibrotactile stimulus duration was set to 100 ms and the inter–stimulus–interval (ISI)

was randomly varied from 400 ms to 430 ms to break rhythmic patterns presentation. In the presented study, the ERP
intervals, for a subsequent classifications, were used in latencies covering 0 ⇠ 800 ms after the stimulus onsets.
After the EEG experiment, the acquired ERP intervals
were post–processed offline using MATLAB software. In
this study, the EEG signal processing was divided into threesteps. At first, the preprocessing began with a bandpass filtering. The filter passband was set at 0.1 ⇠ 30 Hz range to limit
interference noise signals from vibrotactile transducers operating at 40 Hz frequency. Secondly, the filtered ERP intervals
were decimated by 4 ( fs = 128 Hz), 16 ( fs = 32 Hz) and kept
intact at the original sampling frequency of fs = 512 Hz in order to test such feature size reduction process on BCI classification accuracy results. Finally, the decimated ERP intervals
were averaged using 5, 10 and single ERPs to further evaluate
the proposed BCI paradigm from a classification speed angle(the less averaged ERPs the faster the classification). The final results of the decimation factors and averaging numbers
relations to the BCI classification accuracies have been summarized in the following results section.
Before a classifier training, preprocessed ERP intervals
were converted into feature vectors. Single feature vector
was comprised of all concatenated electrode channel ERP intervals. Namely, a feature vector length was calculated as
Vlength = dERPinterval · fs /nd e · nc , where ERPinterval stood forthe duration length of ERP interval in seconds (0.8 s in thisstudy), fs for the sampling frequency (512 Hz in this study),nd for the signal decimation factor (selected one from 1, 4 or16), nc for number of electrode channels (8 in this study), andd·e denoted integer ceiling function. For example, a feature
vector length was Vlength = 824 when the decimation factor
was set to nd = 4.
The concatenated feature vectors were used for the classifier training. The default numbers of feature vectors werevaried from 6 to 60 for targets and 30 to 300 non–targets depends on the number of averaging steps used. The input non–target feature vectors were randomly chosen as many as thenumber of target feature vectors for the class equivalences.
For example, 60 target and non–target feature vectors wereapplied to train the classifier in case of non averaging setting.
The same feature selection settings were applied in the classification phase for both the vector length (Vlength ) and input
feature vector numbers (varying from 1 to 10 for targets and
5 to 50 for non–targets in a single session).
In the presented study, we adopt three machine learning
methods (SWLDA, linear SVM and non–linear Gaussian kernel SVM) to calculate the stimulus pattern classification accuracies. The most suitable classification methods for the tactile
P300–based BCI paradigm was assessed by comparing the resulting accuracies. The SWLDA method has been known so
far as the most efficient technique for the P300 response classification [10, 11]. The SWLDA was developed as a regression model of the Fisher’s linear discriminant analysis (LDA)
by repetitive addition and reduction of features based on resulting statistical tests. The SVM methods have been commonly used not only for brainwave classification [10] but also
in many general machine learning studies. The SVMs havebeen achieving their high discriminant performances basedon maximization intraclass margins. Moreover, SVM clas-

Figure 2: Grand mean averaged ERP results of all ten users in the fbBCI EEG experiment for target (purple lines) and non–target (blue lines) stimulus patterns. The vertical axis of each ERP result shows the electrical potentials, whereas the horizontal
the time series after the stimulus onsets. The red covered area represents the vibrotactile stimulus duration (0 ⇠ 100 ms), where
electrical interferences could be spotted in form of EEG oscilations.

sification could be supported with several kernel functions
K(u, v0 ) depending on the machine learning problems. In thisstudy, we tested a linear (linear SVM) K(u, v0 ) = uT v0 and
a Gaussian (non–linear SVM) K(u, v0 ) = exp( gku v0 k2 )kernels, where u and v were input vectors and g = 1/Vlength
for the P300 response classification. The parameter cost for
the Gaussian kernel was fixed to c = 1.
III. RESULTS
The fbBCI EEG experiment results have been summarized in
Figure 2, as grand mean averaged ERP responses of all ten
participated users. The above figure presents the ERP intervals after stimulus onsets from 0 to 800 ms for all electrodechannels, with clear somatosensory P300 responses. The
most encouraging findings were that the electrical potentials
for the target stimulus patterns reached 4 µV or higher potentials for every electrode and their intervals were longer than
400 ms. These characteristics further assisted the superior
classification of the proposed vibrotactile stimulus patterns.
The vibrotactile stimulus pattern classification accuracy results using the SWLDA, linear and non–linear SVM classifiers of the fbBCI EEG experiment have been reported in Figure 3 as accuracy comparisons with the decimation factors of
4, 16 or non decimation. Also the following averaging sce-narios were tested using 5, 10 or non averaging steps in EEG
signal preprocessing settings. Each bar in Figure 3 represents
grand mean averaged pattern classification accuracy over fiveexperimental trials for all ten participating users. The best
BCI accuracies for each classification method with the following achived rates of 57.48% for SWLDA, 58.5% for linear
and 59.83% for non–linear SVMs, respectively. The best signal preprocessing settings were those using decimation factor
4 and no ERP averaging settings for the both SWLDA and
non–linear SVM, whereas the decimation factor of 16 andnumber of ERP averaging of 10 for linear SVM. It has been
noteworthy that all of the classification accuracies exceededa chance level rate of 16.7% in the six–command based BCI
experiments.
IV. DISCUSSION AND CONCLUSIONSThe purpose of the presented study was to determine themost suitable classification method for a tactile P300–based
BCI paradigm. Besides, from the improved classification
accuracy results, the potential validity of the proposed full–body tactile stimulation–based modality was also confirmed.
The most encouraging finding in this study was the bestclassification accuracy result using the non–linear SVM(59.83%) exceeding the mean accuracy result of our previ-[2] L. P. Rowland and N. A. Shneider, “Amyotrophic lateralsclerosis,” New England Journal of Medicine, vol. 344,
no. 22, pp. 1688–1700, 2001.
[3] E. Donchin and M. G. Coles, “Is the P300 component
a manifestation of context updating,” Behavioral andbrain sciences, vol. 11, no. 3, pp. 357–427, 1988.
[4] G. Pires, M. Castelo-Branco, and U. Nunes, “Visualp300-based bci to steer a wheelchair: a bayesian approach,” in 2008 30th Annual International Conferenceof the IEEE Engineering in Medicine and Biology Society. IEEE, 2008, pp. 658–661.
[5] M. Chang, N. Nishikawa, Z. R. Struzik, K. Mori,
S. Makino, D. Mandic, and T. M. Rutkowski,“Comparison of P300 responses in auditory, visualand audiovisual spatial speller BCI paradigms,” inProceedings of the Fifth International Brain-Computer
Interface Meeting 2013. Asilomar Conference Center,
Pacific Grove, CA USA: Graz University of Technology
Publishing House, Austria, June 3-7, 2013, p. Article
ID: 156. [Online]. Available: http://castor.tugraz.at/
doku/BCIMeeting2013/156.pdf

Figure 3: Comparison of the fbBCI grand mean averaged
pattern classification accuracies using SWLDA (top), linear
SVM (middle) and non–linear SVM (bottom). The verticalaxis shows a percentage of the fbBCI classification accuracies. The horizontal axis represents number of signal averaging and each bar shows the signal decimation factor.
ous study using the SWLDA method (53.67%) [9]. Likewise,
the both current study best results using SWLDA (57.48%)
and using linear SVM (58.5%) exceeded the previously published rate with the similar signal preprocessing settings. The
reported in this paper findings have suggested that the non–linear SVM have been so far a more effective classification method for the tactile P300–based BCI paradigm. They
also indicated a possibility that the proposed full–body tactile
stimulation–based modality shall be applicable for LIS patients who have difficulty using vision or audition sensations
due to their disabilities.
The presented study, however, was only conducted on the
full–body tactile BCI modality with ten healthy users till
now. Therefore, more analyses would be required, for example, more detailed comparison with another results of tactile
P300–based BCI studies, or evaluation with disabled users.
Overall, the results have reconfirmed that the P300 response–
based full–body tactile BCI paradigm shall be a practicalmethod. We expect that in the near future this neurotechnology application will contribute to improve a quality of life forthose suffering from ALS and LIS disease patients in need.
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