2016 AEARU Young Researchers International Conference
September 17-19, 2016, University of Tsukuba, Japan
Research

Toward a QoL improvement of ALS patients: Development of the Full–body P300–based Tactile
Brain–Computer Interface
Takumi Kodama† , Shoji Makino† and Tomasz M. Rutkowski‡?
† Life Science Center of TARA, University of Tsukuba, Tsukuba, Japan‡ The University of Tokyo, Tokyo, Japan? RIKEN Brain Science Institute, Wako-shi, Japane-mail: {takumi, tomek}@bci-lab.info

Abstract—We propose and validate the effectiveness ofa novel stimulus–driven brain–computer interface (BCI)
paradigm, which generates control commands based onsomatosensory P300 responses classification. For BCI purposes, six spatial vibrotactile stimulus patterns are given
to the user’s entire back and limbs, defining as the full–body BCI (fbBCI). We conduct the electroencephalogram(EEG) response classification experiments with ten body–able users. The overall accuracy of stimulus pattern classification accuracy was 59.83% with the non–linear SVM
classifier. The aim of the current project is to establish
a novel concept of tactile modality communication link,
which shall help locked–in syndrome (LIS) patients wholose their sight and hearing due to sensory disabilities.
Keywords—Brain–computer interface (BCI); EEG; Tactile P300–based BCI; Human–computer interaction(HCI).

I. INTRODUCTION
Past decades have seen the rapid development ofa brain–computer interface (BCI) neurotechnology. The
BCI is a human computer–interaction technique, whichenables people to express their thoughts through any
computer devices without their muscle movements [1].
Achievements of this interface contributes to a life
improvement of amyotrophic lateral sclerosis (ALS) patients, who have difficulty to move their muscles due to aneuromotor disabilities, which prohibit them from living
their lives freely [2], [3].
In this study, we examine a P300 response–based BCIparadigm using a touch sensation, in other words, atactile BCI [4]. The tactile BCI could be applicable tolocked–in syndrome (LIS) patients who lose their sightand hearing as a late symptom of the ALS. Therefore,
an establishment of this alternative paradigm will provide not only a better communication method for careworkers, but also improve a patient quality of life. Recenttactile BCI studies have reported practical feasibility ofa tactile stimulus for creating an alternative P300–based
BCI paradigm [4].
It should be noted, however, that most of the tactile
BCI studies have only been carried out in the limitedareas of the human body, such as hands, fingers or arounda head [5], [6]. The problem here is that some totally
locked–in (TLS) patients may be not able to utilize suchtypes of modalities due to impaired afferent neural fibers
in those specific areas of their bodies. Furthermore, inorder to help the users more easily distinguish thosetactile stimuli, it shall be more practical to place thevibrotactile transducers in spatially distant locations,which means with a larger distance apart from eachother [7], [8].
Accordingly, we present a study of the novel P300–
based tactile BCI, in which spatial vibrotactile stimulus
patterns are applied to the user’s entire back and limbs
in order to evoke the somatosensory P300 responses.
We define this modality as the full body tactile BCI(fbBCI). The vibrotactile transducers are placed withlarger distances on a mattress in order to give tactilestimulus patterns to the user. The fbBCI is designed fora practical application for bedridden patients so the user
could test it with their body lying down on the mattress.
Finally, the somatosensory P300 responses are detected by the non–linear SVM classification algorithm
and they are translated into interfacing commands.
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 withguidelines and permission of the institutional ethicalcommittee, as well as in accordance with The World
Medical Association Declaration of Helsinki - EthicalTABLE I: Conditions of the EEG experiment
Condition
Number of users
Users mean age
Stimulus generators(a)

Stimulus frequency
EEG recording system
EEG electrode positions
EEG sampling rate
EEG acquisition environment
Target stimulus lengthInter–stimulus interval (ISI)
ERP interval(b)Detail10 (5 males and 5 females)
21.9 years old
Dayton Audio TT25-16 transducers
40 Hz
g.USBamp active electrodesEEG system
Cz, Pz, P3, P4, C3, C4, CP5,and CP6
512 Hz
BCI2000
100 ms400 ∼ 430 ms0 ∼ 800 ms after stimulus
onsets(c)Fig. 1: The experimental apparatus and conditions. Panel (a)presents the fbBCI user lying down on a Japanese–style mattress with embedded vibrotactile transducers. The user havebeen instructed to distinguish various vibrotactile stimulus
patterns given to his entire back and limbs throughout thefbBCI experiment. The photograph was included with the userpermission. Panel (b) presents a vibrotactile transducer Dayton
Audio TT25-16 employed in the current study to generate
vibration stimuli within the fbBCI paradigm. Panel (c) depicts
eight vibrotactile transducers placed on the mattress to create
six fbBCI stimulus patterns, which are #1 left arm; #2 rightarm; #3 shoulder; #4 waist; #5 left leg; and #6 right leg,
respectively. A longer distance among the transducers is usedcomparing to usual hand or facial area applications.

Principles for Medical Research Involving Human Subjects. All the participating users were paid for theircontribution and provided informed consents.
The participating users were asked to lay down ina silent environment on a Japanese–style mattress containing a polyester filling as presented in Figure 1a.
Eight vibrotactile transducers Dayton Audio TT25-16, as
depicted in Figure 1b were embedded on the mattress tocreate six fbBCI stimulus patterns transmitted to arms,shoulder, waist and legs as shown in Figure 1c. Twotransducers were applied to the shoulder and waist, andonly single one to each arm and leg. The stimulus carrier
frequencies of the transducers were set at 40 Hz.
A bio–signal amplifier system g.USBamp from g.tec
Medical Engineering GmbH, Austria, was employed torecord the EEG signals. Following the 10/10 extended
international system, active g.LADYbird electrodes wereattached to Cz, Pz, P3, P4, C3, C4, CP5 and CP6 tohead locations to cover the primary somatosensory andparietal cortices. A reference electrode was attached to

the left earlobe, and a ground electrode to the head FPzposition. The EEG recording sampling frequency was set
at 512 Hz. 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 were used in latencies covering 0 ∼ 800 ms
after the stimulus onsets. Details of the fbBCI EEGexperimental protocol are summarized in Table I.
In each fbBCI single experimental session 10 targets
and 50 non–targets stimulus patterns were randomly
presented to the users. The sessions were repeated until
each of the six stimulus pattern became targets, namely60 targets and 300 non–targets were presented overall
in a single experimental trial. Each user participated infive trials in a row and the stimulus pattern classification
accuracies were calculated by averaging all of the fivetrials.
After the EEG experiment, the acquired ERP intervals
were post–processed and classified in offline. 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. Then, the filtered ERP
intervals were decimated by nd = 4 (fs = 128 Hz).
Finally, preprocessed ERP intervals were converted intofeature vectors. Single feature vector was comprised ofa concatenation of all electrode channel ERP intervals.
A feature vector length can be calculated as Vlength =ceil(ERPinterval · fs /nd ) · nc where ERPinterval stoodfor the duration length (second) of ERP interval (0.8in this study), fs represented the original sampling
frequency (512 Hz in this study), nd was the signal
decimation factor (4 in this study) and nc was the numberon maximization intraclass margins. Moreover, SVM
classification could be supported with several kernel
functions K(u, v 0 ) depending on the machine learning
problems. In this study, we tested the Gaussian kernel
1K(u, v 0 ) = exp(−γku − v 0 k2 ), where γ = Vlengthfor the somatosensory P300 response classification. The
parameter cost for the Gaussian kernel was fixed toc = 1. All the preprocessing and classification flows aresummarized in Figure 2.
III. RESULTS

Fig. 2: The ERP intervals acquired by eight EEG electrodes
are post–processed and classified in BCI systems. After thebandpass flilterling and downsampling is taken, the ERP
intervals are converted into features vectors. Based on thefeature vectors, user’s intentions can be predicted by the nonlinear SVM classifier.

of electrode channels (8 in this study). Namely, a featurevector length was Vlength = 824 in this study.
The concatenated feature vectors were used for theclassifier training. The default numbers of feature vectors
were 60 for targets and 300 non–targets. The input non–target feature vectors were randomly chosen as many as
the number of target feature vectors for the class equivalences (to avoid a classifier overfitting during trainings).
In this case, 60 target and 60 non–target feature vectors
were applied to train the classifier. The same featureselection settings were applied in the classification phasefor both the vector length Vlength and input feature vector
numbers (10 for targets and 50 for non–targets in a single
session).
In the presented study, we adopt non–linear SVM tocalculate the stimulus pattern classification accuracies.
The SVM methods have been commonly used not only
for brainwaves classification [9] but also in many general machine learning studies. The SVMs have beenachieving their high discriminant performances based

The grand mean averaged ERP intervals of all theten users participating in the fbBCI experiments havebeen depicted in Figure 3. In each electrode position, somatosensory P300 responses were confirmed in latency
ranges from 200 ms to 600 ms after the target stimulus
patterns presented. The most encouraging findings werethat the electrical potentials for the target stimulus patterns reached around 5 µV or higher potentials and theirintervals were longer than 400 ms. These characteristics
further assisted the superior classification of the proposed
vibrotactile stimulus patterns.
The mean vibrotactile pattern classification accuracy
results using non–linear SVM classifier was 59.83%
by avaraging all the ten participant users. It has been
noteworthy that the classification accuracy exceeded achance level rate of 16.7% in the six–command based
BCI experiments. Overall, the experimental results havebeen very encouraging and proven an effectiveness ofthe proposed novel P300 response–based tactile BCIparadigm.
IV. DISCUSSION AND CONCLUSIONSThis project was undertaken to verify the effectiveness of the proposed vibrotactile stimulus patterns given
to user’s entire back and limbs for BCI–based interaction
purposes. The main goal of the reported project was todesign the novel P300 EEG response–based tactile BCIparadigm.
The mean vibrotactile pattern classification accuracy
was of 59.83% in the proposed EEG experiments. The
results were not yet fully satisfactory, yet as comparedto the competitive visual or auditory BCI paradigms,
the outcomes were considered as good. Consequently,
the reported findings in this paper have indicated apossibility that the proposed modality shall be applicablefor LIS patients who have difficulty using vision oraudition sensations due to their disabilities.
The presented study, however, was only conducted on
the full–body tactile BCI modality with ten healthy users

Fig. 3: 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 thetime series after the stimulus onsets. The red covered area represents the vibrotactile stimulus duration (0 ∼ 100 ms), whereelectrical interferences could be spotted in form of EEG oscilations.

till now. Therefore, more analyses would be required, forexample, more detailed comparison with another resultsof tactile P300–based BCI studies, or evaluation withdisabled users.
Overall, the results from the presented full–body tactile BCI study were encouraging. They demonstrated that
the P300 response–based full–body tactile BCI paradigm
has proven to be a viable method. We are confident that
future developments in this field will help improve thequality of life of those patients in need who cannot relyon vision or audition–based modalities.
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