eeg-controlledwall-crawlingcleaningrobotusing ssvep-basedbrain-computerinterface · 2019. 8....

Research ArticleEEG-Controlled Wall-Crawling Cleaning Robot UsingSSVEP-Based Brain-Computer Interface

Lei Shao1 Longyu Zhang1 Abdelkader Nasreddine Belkacem 2 Yiming Zhang1

Xiaoqi Chen1 Ji Li 1 and Hongli Liu1

1Key Laboratory for Control eory amp Applications in Complicated Systems Tianjin University of TechnologyTianjin 300384 China2Department of Computer and Network Engineering College of Information Technology United Arab Emirates UniversityAl Ain PO Box 15551 UAE

Correspondence should be addressed to Abdelkader Nasreddine Belkacem belkacemuaeuacae

Received 13 August 2019 Revised 29 October 2019 Accepted 29 November 2019 Published 11 January 2020

Guest Editor Ludovico Minati

Copyright copy 2020 Lei Shao et alis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

e assistive adaptive and rehabilitative applications of EEG-based robot control and navigation are undergoing a majortransformation in dimension as well as scope Under the background of artificial intelligence medical and nonmedical robots haverapidly developed and have gradually been applied to enhance the quality of peoplersquos lives We focus on connecting the brain witha mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer thepromise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomymobility and abilities Several types of robots have been controlled using BCI systems to complete real-time simple andorcomplicated tasks with high performances In this paper a new EEG-based intelligent teleoperation system was designed for amobile wall-crawling cleaning robot is robot uses crawler type instead of the traditional wheel type to be used for window orfloor cleaning For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning weextracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal e visualstimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (eg 6 Hz75Hz 857Hz and 10Hz) Seven subjects were able to smoothly control the movement directions of the cleaning robot bylooking at the corresponding flicker using their brain activity To solve the multiclass problem thereby achieving the purpose ofcleaning the wall within a short period the canonical correlation analysis (CCA) classification algorithm had been used Offlineand online experiments were held to analyzeclassify EEG signals and use them as real-time commandse proposed system wasefficient in the classification and control phases with an obtained accuracy of 8992 and had an efficient response speed andtiming with a bit rate of 2223 bitsmin ese results suggested that the proposed EEG-based clean robot system is promising forsmart home control in terms of completing the tasks of cleaning the walls with efficiency safety and robustness

1 Introduction

e idea of interfacing brains with machinesrobots has longcaptured the human imagination Brain-computer interface(BCI) technology intend to build an interface between thebrain and any electricalelectronic device (eg a wheelchairsmart home appliances and robotic devices) using elec-troencephalogram (EEG) which is a noninvasive techniquefor measuring electrical potentials from electrodes placed onthe scalp produced by brain activity Nowadays the EEG

technique has been used to establish portable synchronousand asynchronous controls for BCI applications Nonin-vasive EEG-based BCIs are the most promising interface forspace of applications for people with severe motor dis-abilities because of their noninvasiveness low cost practi-cality portability and being easy to use For some disabledpatients with physical disability or paralysis while the brainfunction is still normal although they have a normal largebrain consciousness and thought they cannot communicatewith the external environment through the severely

HindawiJournal of Healthcare EngineeringVolume 2020 Article ID 6968713 11 pageshttpsdoiorg10115520206968713

damaged muscle and nervous system and complete the dailywork independently is has caused serious physical andmental trauma and their lives are very painful which willaffect their recovery process to some extent How to restoreor enhance their control and communication capabilities tothe outside world has been the goal that has been pursued formany years in the field of medical rehabilitation ereforeBCIs can be used for helping patients with severe braindisorders or muscle damages to regain their ability tocommunicate directly with the outside environmentthrough the brain electrophysiology response [1ndash3] BCI canalso be beneficial for the elderly as advanced assistive andrehabilitative technologies and useful for young able-bodiedfor controlling video games for entertainment [4 5] orcontrolling a robotic arm for several purposes [6ndash9]However most of the traditional brain-computer interfaceequipment is expensive bulky and tedious which makes itdifficult to popularize and apply brain-computer interfacetechnology in real life e portable brain-computer inter-face has become one of the hotspots in the field of the brain-computer interface because of its advantages of easy to carryeasy to use safe and reliable

BCI technology is mainly divided into two types ofbrain activity measurement invasive BCI and noninvasiveBCI depending on the way of putting the electrodes torecord the electrical brain activity [10ndash14] Among themthe invasive BCI might lead to an immune reaction whichcauses serious harm to the user and it is hardly accepted bydisabled people because of the invasiveness of the tech-nique which requires a dedicated surgery and its cost withequipment is very expensive and not covered by manygovernments yet Although the noninvasive brain-com-puter interface is less accurate than the invasive BCI it isstill relatively cheaper compared with all other techniquesand everyone can easily accept it ere are several para-digms to control machine or computer using our brainsignal characteristics and the most popular ones are motorimagery [15 16] P300 wave [17 18] steady state visualevoked potentials (SSVEP) [19ndash21] for building practicalbrain-computer interface systems So far the SSVEPmethod was applied widely because of the high signal-to-noise ratio and robustness [22] SSVEP induction meansthat when the human brain receives the stimulation of afixed frequency scintillation block an uninterrupted re-sponse related to the stimulation frequency will be gen-erated in the visual cortex of the human brain is SSVEPbrain response is a very useful natural involuntary phe-nomenon which has been tested by researchers many timese earliest SSVEP-BCI system designed by Regan in1979 allowed subjects to select a flashing button on thecomputer screen by simply looking at the computer screen[23] basically achieving the desired design goals enMullerputz and Guneysu and Akin applied the SSVEP-BCIsystem to the physical control of neural limb and humanoidrobot respectively and achieved good control results [24]In this paper we chose SSVEP because it does not need anytraining phase for subjects and has very high accuracycompared with P300 or motor imagery using single trialelectroencephalography (EEG) signal e commonly used

signal processing and classification methods of SSVEPinclude fast Fourier transform (FFT) wavelet transformcanonical correlation analysis (CCA) linear discriminantanalysis (LDA) and support vector machine (SVM) In thispaper CCA was used for developing our signal processingalgorithm Compared with other SSVEP signal classifica-tion algorithms [10ndash14 25] CCA classification algorithm isfast efficient simple and easy to use

In some previous researches the SSVEP paradigm wassuccessfully used in writing tasks [26] In the paper [27] wecan see that the authors proposed a hybrid brain-computerinterface system that combines P300 and SSVEP modalitiesis combined system has improved the accuracy of EEG-based wheelchair control In addition SSVEP has been alsoused in the mental spelling system [28 29] In the paper [30]the authors used three flash speeds to control the small robotcar Lee et al only use OZ as the reference electrode to collectand process EEG signals In the paper [31] Lu and Bi haveproposed a longitudinal control system for brain-controlledvehicles based on EEG signals However it is still unknownwhether it can be used in the industry

In this paper a new type of intelligent crawler robot isdesigned for cleaning the walls which is considered as one ofthe smart home appliances Compared with the wheeledrobot [32] the crawler robot has the advantages of long lifeand high carrying capacitye intelligent crawling robot forthe walls used in this experiment adds an adsorption deviceusing negative vacuum pressure which effectively solves theproblem of sliding of the cleaning robot on a wall with acertain inclination angle e BCI based on SSVEP canusually provide a high information transmission rate theverification process of the system is relatively simple andno training of the subjects is required However becausethe SSVEP of some subjects is very weak and vulnerable tothe interference of other noise signals how to accuratelyidentify SSVEP from a short time window is still a chal-lenging problem in BCI research based on SSVEP is isalso the subject that we will continue to study in the futureIn this study the SSVEP paradigm was designed to controlthe crawler robot for cleaning the dust on the walls Weused the high accuracy SSVEP paradigm to cooperate withour cleaning robot to complete the designed experimentTo our best knowledge this is the first report which usedbrain machine interface for crawling cleaning robot controlto help persons with disabilities to improve their quality oflife

is paper is arranged as follows in the Materials andMethods section the experimental paradigm and analysismethod of brain signal and the motion model of the in-telligent crawling robot were introduced At the same timethe offline experiment and online experiment are completedand the data analysis is carried out In the Results section theoffline and online experiments were summarized and dis-cussed separately and the accuracy and ITR of the exper-iment were obtained Our experiments validate our viewsand achieve the desired results In the Discussion part wemainly talk about the limitations of the system and putforward the future changes Finally conclusion and pros-pects of future work are given in Section 5

2 Journal of Healthcare Engineering

2 Materials and Methods

21 Participants and Experimental Paradigm Seven healthyvolunteers (4 males and 3 females 23ndash27 years of age) wereinvited to join the experiment for performing some robotcontrol tasks using their brain activity None of the subjectshave prior experience on brain-computer interfaces Clearwritten informed consent was obtained from all the par-ticipants who were informed in detail about the purpose andpossible consequences of the experiment e experimentalprotocol was carried out in accordance with the latestversion of the Declaration of Helsinki

e experiments were carried out in a quiet and com-fortable environment to reduce the noise effect on our EEGrecording Subjects sat on a chair which is 60 cm away fromthe screen which contains the stimulation interface In orderto ensure the accuracy of the experiment participants wererequired to avoid gnashing during the experiment Becausethe SSVEP paradigm was easy to cause fatigue the subjectscan take a rest after one sessione flow of the experiment isshown in Figure 1 e experimental process is mainly di-vided into three parts Firstly the EEG acquisition deviceshould be worn correctly for the subject and the subjectrsquosposition should be adjusted Secondly the collected data areprocessed and classified by a signal processing computerFinally the processed instructions are sent to the lowercomputer that is the intelligent crawling robot

22 Experimental Materials As shown in Figure 1 thehardware system in this experiment mainly includes fiveparts EEG signal acquisition system (Brain ProductsGermany) computer for displaying visual stimulation in-terface computer for signal processing Bluetooth modulefor transmitting signals wirelessly and intelligent crawlingrobot for cleaning dust on the walls

We have avoided in this study to use EMOTIVE EPOCequipment which is relatively cheap consumer-grade EEGsignal acquisition device because its measurement signalquality is not good enough for getting high classificationaccuracy using SSVEP modality On the contrary BrainProducts can effectively collect the EEG signals induced bySSVEP record real-time characteristic signals and havegood effects EEG equipment (Brain Products) has the ad-vantages of lightweight flexible usage and excellent andstable signals erefore this EEG equipment was used tocollect brain signals in our experiment e EEG signalacquisition device used in our experiment is shown inFigure 2

e EEG signal acquisition device selected in the ex-periment consists of 64 electrodes 32 black circles representeffective electrodes and white circles are invalid electrodesAll 32 channel electrodes which include 30 EEG signalsacquisition channels 1 reference channel and 1 groundchannel were used to record brain signal e position ofeach channel was shown by black circles e sampling ratewas 500Hz During the experiment the impedance of eachchannel was always below 10 k ohms to ensure the quality ofEEG signals Because the SSVEP signal is generated by the

visual cortex of the brain the EEG signals of O1 O2 P7 andP8 channels near the visual cortex are collected in the ex-periment which will not affect the acquisition of SSVEPsignal but also greatly reduce the amount of data processedby EEG

e stimulation interface of the experimental paradigmwas designed by using MATLAB psychology toolbox isinterface contains four blocks which flash at frequencies6Hz 75Hz 857Hz and 10Hz respectively four blockswere shown in the top bottom left and right part of thescreen and the refresh rate of the screen was set in 60Hze driving chip of the intelligent component used in theexperiment is the L298P double H-bridge DC motor drivingchip which integrates most of the functions making thechip more suitable for robot development We designed anew crawler robot and also upgraded the adsorption ca-pabilities of this robot e traditional suction cup has asmall adsorption capacity and is unstable so the adsorptiondevice we use uses a vacuum pump to generate a negativepressure to avoid the disadvantages of the conventionalsuction cup Most of the photovoltaic robots on the marketare roller brushes e roller brushes are not only easy toabsorb dust but also occupy a relatively large area of therobot erefore the crawling cleaning robot we use uses athree-legged brush head After a series of experiments themost suitable for the cleaning of the walls is to use a motorwith a speed of 200 rpm to control our three-legged brushhead e robot relies on the suction cup to be adsorbed onthe walls and the walls can be stably and selectively cleanedthereby reducing the trouble of cleaning the entire walls

221 Offline Experiment e whole experiment was di-vided into offline and online subexperiments Offline ex-periment was held to confirm the experiment set up and

Serial port

Bluetooth

Signal acquisition

Sign

al p

roce

ssin

g

Signal acceptanceRobot commands

Figure 1 Experimental flowchart for our proposed SSVEP EEG-based BCI for robot control system

Journal of Healthcare Engineering 3

adjust the parameter for each subjecte offline experimentsteps are as follows start our stimulation interface whichstarts with an exclamation mark to remind the subjects tostart the experiment After that the four different frequencyblocks on the top bottom left and right of the screen start toflicker During the offline experiment the subjects listenedto the arrangement to look at each of the four differentfrequencies blocks e subjects were assigned to look ateach of the four scintillation blocks for five times and theduration of each time was between 20 and 25 secondsTwenty datasets were collected for each subject

222 Online Experiment e experimental process of thesubjects is shown in Figure 3 First start our cleaning robotand turn on the cleaning device Before the online experi-ment we must ensure that the subject is in a comfortableposition and that the EEG signal collection cap is correctlyworn Secondly ensure that the cleaning robot is connectedto our upper computer normally In addition the speed ofthe cleaning robot is set After that we started our onlineexperiment In the online experiment the subjects confirmthe position of the cleaning robot and the dirty place on thewall en the subjects make the decision which directs theclean robot to move and look at the responding flash blockon the screen At the same time the EEG data were recordedfrom the participants then it was analyzed at the hostcomputer e algorithm could recognize which block thesubject looked at and transform the control command of theclean robot en the command which was recognized wassent to the clean robot by the Bluetooth module Accordingto the command the clean robot will move and make thewalls cleaner

To evaluate the experiment performance more easily weset up several groups of online experiments First the cleanrobot was put on the wall and the dirty place was not so faraway from the original place of the clean robot erefore

the subjects can control the clean robot to reach the dirtyplace within 30 steps with optimized route as shown inFigure 4

is figure shows the path of a subject during an onlineexperiment For each session the subject was limited toperform 30 steps If the subject cannot control the cleanrobot to reach the destination this experiment will finishand the task and system performance will be evaluated

23 Signal Acquisition and Processing e flowchart ofsignal acquisition and processing is shown in Figure 5During our experiment we used BrainVision Recordersoftware to record the EEG signals of the subjects Whenusing BrainVision Recorder software we ensure that theimpedance of the subjectsrsquo electrodes is below 10 k ohms P7P8 O1 and O2 channels which cover the vision areas of thebrain were mainly analyzed

Wavelet transform was employed as band-pass filterremoving DC Component of Signal For SSVEP paradigmcanonical correlation analysis is a multivariate statisticalanalysis method that reflects the overall correlation betweentwo groups of indicators by using the correlation betweenpairs of comprehensive variables it has a good recognitioneffect in multichannel EEG signals Compared with otherSSVEP signal classification algorithms CCA classificationalgorithm is fast efficient simple and easy to use In thepaper [33] the CCA classification algorithm is comparedwith the power spectral density analysis (PSDA) e testresults show that the classification accuracy of CCA clas-sification algorithm is higher than that of PSDA In the paper[34] the CCA classification algorithm is compared with theminimum energy combination (MEC) and the anti-inter-ference ability of the CCA classification algorithm is foundto be stronger ese results fully demonstrate the reliabilityof the CCA classification algorithm erefore CCA isapplied to the brain-computer interface system based on

EOG

EOG

P3 Pz P4P8

Fp2Fp1

F7

T7 C3

FC1

F3

10cm red

FzF4

F8

Ref FC2 FC6

Cz C4 T8

CP5CP1 CP2

CP6TP10

O1 Oz

TP9

P7

FC5

O2

Gnd30cm green

Figure 2 EEG electrodes placement used in our experiment using Brain Products equipment


Represents the location of dust

Represents the initial position of the cleaning robot

Represents the running track of the cleaning robot

Figure 4 e online experimental paradigm with respect to the path of a representative subject

EEG data collection andstorage in output-matrix

Construct fourreference variables

EEG signalpreprocessing

Forming the matrix tobe tested (P7 O1

O2 P8)

Eliminate DCcomponents perform

wavelet denoisingfast Fourier filtering

Canonical correlationanalysis (CCA)

Get four correlationcoefficients

Judging the subjectrsquosintention outputtingcontrol instructions

Correlationcoefficient gt Threshold

Yes

No

Task

Idle

Signal acquisition process

Signal processing

Figure 5 Signal analysis and processing steps of the proposed algorithm for decoding SSVEP from the EEG signal

Subject

Determinethe robotrsquospositionand the

location ofthe dust

Decide onthe

instructionsto be sent

EEG Trigger a commandCompletean action

Crawling robotperforms the action

Yes

NoNo

Yes

Figure 3 Flowchart of the subject performing a brain control task


SSVEP After preprocessing CCA was used to analyze thebrain signal

One set of EEG signals is denoted as x(t) and thesecond set of signals y(t) is composed of signals with thesame number of stimulation frequencies We decomposea series of periodic signals into a series of Fourierfunctions For a specific frequency f there is the followingequation

Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn

middot middot middot

sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)

where N is the number of sampling points fs is the samplingfrequency and Nh is the number of harmonics

e feature extraction method of CCA is shown inFigure 6 Suppose that there are two groups of sample signalsX (x1 xn) Y (y1 ym) and the linear combi-nation of x XTWx y YTWy Canonical correlationanalysis method calculated the correlation ρ(x y) between xand y under the condition that the coefficientsWx andWy ismaximum e equation was shown as follows

maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT

xXXTWx1113858 1113859E WTyYYTWy1113960 1113961

1113969

(2)

e correlation coefficient between the brain signal andfour classes was computed erefore the control commandwas set as the class with the maximum correlationcoefficient

In this study information transmission rate (ITR) wascalculated to evaluate the transmission performance of brainmachine system ITR (bitmin) can be calculated by thefollowing formula

ITR 60t

log2N + Acc log2 Acc +(1 minus Acc)log2 Acc1 minus AccN minus 1

1113890 1113891

(3)

where t represents the sampling time Acc represents thecorrect rate and N represents the number of classifications

24 Motion Model Analysis of Intelligent Crawling Robote intelligent crawling robot we use communicates with theupper computer through the Bluetoothmodulee speed ofthe cleaning robot can be set according to needs usingBluetooth serial port assistant Among them the runningspeed of the cleaning robot affects its turning angle eturning motion model is as follows

(i) We set a time parameter t when t 0 the robot is inthe original position assuming that the position ofthe cleaning robot coincides with the coordinatesystem X O Y and the coordinates of the robotare XR0 OR0 YR01113864 1113865 as shown in the blue position inFigure 7

(ii) After that the robot performs a turning actionWhere the time is t the robot reaches a new positionwith coordinates of XRt ORt YRt1113864 1113865 as shown in thered position in Figure 7

e whole turning process of the robot takes the origin asthe center For the turning motion of the robot for any timet we have the following

x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)

where ]L is the speed of the left wheel of the crawling robot]R is the speed of the right wheel of the crawling robot and θrepresents the rotation angle of the crawling robot Weassume that ]L minus ]R at this time the crawling robot makesa rotation movement with the origin as the center and therotation angle is as follows

θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)

where D is the distance between the two crawler wheels ofthe crawling robot

3 Results

31 Offline Experiment and Result Analysis For offline useexperiment data was saved in header file ldquovhdrrdquo andEEGLAB toolbox was used to process the data In order toachieve a good effect in our online experiment we needto process the offline experiment data of different sub-jects and adjust the parameters e CCA thresholddivides the state of subjects into idle state and task stateBy comparing the CCA correlation coefficient of thesubject in the idle state with the CCA correlation coef-ficient in the task state we can determine the threshold ofeach subject We adjusted the subject threshold based onthe analysis of the subjectsrsquo offline data which wereshown in Table 1

32OnlineExperiment andResultAnalysis In order to makeour experimental data more accurate when the subjectcompletes the cleaning task within 30 steps the time is


stopped when the task is completed but the subjectcontinues to perform the task until the end of the 30 stepsWhen the subject does not complete the task within 30steps the time is counted based on the time when 30 stepsare completed Each subject has to perform six sets oftasks

After the experiment we performed some statisticalanalysis methods to evaluate the subjectsrsquo completion of thetasks Four people were able to complete all the tasks withhigh accuracy and precision e statistic results are shownin Table 1 e experimental data showed that the averageaccuracy of the subjects was 8992 (with standard deviationof plusmn381) and the ITR reached 2223plusmn 119 bitsmin eexperimental situation of the subjects is shown in Table 2 Inorder to further verify the reliability of our experimentalresults and make the data statistically significant we per-formed the variance calculation and the variance calculation

results are shown in Table 3 Accuracy in Table 3 is theaverage of the ratio of the total number of correct commandsto the total number of commands in six experiments perperson

We noticed that our online experiments seem to havegood output results compared to the existing BCIs in termsof task accuracy and ITR We have listed the graphs forintuitive statistics and comparisons as shown in Figure 8ese results show that our experiments validate our viewsand achieve the desired results

4 Discussion

In this paper EEG-controlled wall-crawling cleaning robotusing SSVEP brain-computer interface is proposed and theCCA algorithm is used for signal analysis In this engi-neering study the experiment results showed that our

Multichannel EEG signalX

Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip

Figure 6 Feature extraction phase of CCA

X

Y

OOR0

OR1

YR0

XR0

YR1 XR1

The blue color represents the starting state of the crawling robotThe red color represents the turning of the crawling robot

Figure 7 Moving model of the crawler robot turning


proposed brain-computer interface system is very promisingand could control the proposed designed intelligent cleanrobot successfully to complete the cleaning tasks of a walle offline experiment provided the threshold for the nextexperiments In addition the test range of different exper-imenters can be determined by the same offline experimente online experiment was used to adjust the threshold toimprove the classification accuracy of the experiment Afteranalyzing and calculating the data we got the followingresults the average accuracy of online experiment was8992 and the ITR reached 2223 bitsmin is shows thatour experiments validate our research hypothesis andachieve the desired target However there are still someissues and limitations in the proposed BCI system

emain innovation of this paper is to design a new typeof cleaning robot that can enhance the abilities of the elderlyusers and help handicapped patients to control home ap-pliances that might be available in their usual environmentto increase their personal autonomy to be able to performdaily activities Practicing EEG-based control in daily lifemight be a good option for enhancing brain abilities tooHowever eye movements are also another option instead ofusing brain activity

Electrooculography (EOG) is a technique for measur-ing the corneo-retinal standing potential that exists be-tween the front and the back of the human eye istechnique has been widely used in developing humanmachine interfaces and it can be easily combined with brainactivity However the signal of eye movementsblinking isrelatively not weak erefore it is difficult to remove thiselectrical interference due to the synchronization with EEGsignal If we use the eye movements directly to control thecleaning robot we need to add eye gazetracking equip-ment which needs to be well calibrated In the process ofeye movementsrsquo acquisition the structure of the human eyeleads to fatigue In addition the users are unable to look atthe same target point for a long time is long gazeconcentration leads to the eye movements the false eyejump and the unconsciousness blinking [35] ese er-roneous and unconscious EEG signals will bring difficultiesto feature extraction and classification resulting in a lowrecognition rate [36 37]

In this paper SSVEP is mainly used to overcome theshortcomings of using only eye movement instrumentMoreover the combination of EEG and EOG (eye move-ment) as an innovative research for building hybrid BCIs isthe direction of our future consideration

After Hotelling put forward the typical classical algo-rithm in 1936 it has received great attention in variousresearch fields Some Japanese researchers adopted thecanonical correlation analysis (CCA)method to extract twolayers of reference signals from the actual SSVEP signaltraining concentration Combining the obtained referencesignals with CCA an effective spatial filter for frequencyrecognition is derived which greatly improves the recog-nition accuracy and information transmission rate ofSSVEP [34] Twelve categories of SSVEP signals weregenerated by modulating waveforms and were analyzed byCCA algorithm with an average accuracy of 9231 [38]Obviously the application of CCA in EEG signal pro-cessing has been quite common especially the frequencyrecognition of SSVEP signal which has high accuracy Inthis paper the traditional classical analysis algorithm isused CCA algorithm is used to analyze SSVEP the cor-relation coefficients between brain signal and four kinds ofbrain signal are extracted and calculated and the trans-mission rate and the accuracy of online experiment arecalculated Although the CCA algorithm used in this paperhas great advantages in SSVEP compared with SVMmethod [25] it still needs to be improved Because thehuman brain has complex neural mechanisms it may notbe a simple linear transformation in the transmission ofelectrical signals in the brain In addition to the time andfrequency characteristics that we usually consider EEGalso contains other important data characteristics such asthe variability between experiments and the specificitybetween subjects [12 39] erefore improving featureextraction and classification algorithm will be the focus ofour future research to improve the robustness and accuracyof the system and reduce errors

Table 1 Coefficients for different SSVEP states

SSVEP state Meanplusmn SD6Hz 040plusmn 01275Hz 044plusmn 009857Hz 050plusmn 01010Hz 051plusmn 005Idle 016plusmn 004

Table 2 Experimental statistics

Subject Number of completed tasks e average timeS1 6 6prime25PrimeS2 6 6prime56PrimeS3 6 6prime17PrimeS4 6 5prime59PrimeS5 4 6prime00PrimeS6 5 6prime56PrimeS7 4 7prime00Prime

Table 3 Data statistics of subjects

Subject Accuracy () ITR (bitsmin) VarianceS1 9111 2252 988S2 8945 2226 1265S3 9111 2252 616S4 9111 2252 988S5 9167 2261 1389S6 8889 2217 1357S7 8611 2104 525Average 8992plusmn 381 2223plusmn 119 1018plusmn 493


5 Conclusion

In this paper we proposed a new experimental paradigmfor EEG-based clean robot control which extended theusage of brain-computer interface Compared with otherparadigms like motor imager and P300 SSVEP is betterfor real-time application because SSVEP-based paradigmis not a subject-specific BCI which requires individualdata calibration regularly and system training and it hasachieved higher accuracy across subjects For differentsubjects the corresponding conditions are also differentDuring the experiment we have selected the appropriateEEG acquisition cap according to different subjects Inaddition the fatigue issue was clearly observed in somesubjects erefore we eliminated the inaccurate exper-imental data caused Although the conditions of differentsubjects are different we also verified our system throughour experiments

Noninvasive brain-computer interface technology hasbuilt a bridge between human brain and smart robots whichhas important research significance In the near future dailylife BCI applications will be involved in fields that are morenew With the development of science and technologybrain-computer interface technology will not only bringhope to people with disabilities but will also be more in-tegrated into the life of ordinary people bringing moreconvenience and use to our liferough this experiment wehope that in the future we can recruit more subjects to verifythe proposed system and make complex system in different

conditions such as controlling a swarm of cleaning robots byone operator brain only

Data Availability

e datasets generated and analyzed during the currentstudy are not publicly available due to Tianjin University ofTechnology policy but are available from the first authorupon reasonable request

Conflicts of Interest

e authors declare that there are no conflicts of interestsregarding the publication of this paper

Acknowledgments

Part of this study was funded by the United Arab EmiratesUniversity (start-up grant G00003270 ldquo31T130rdquo)

References

[1] D M Taylor S I H Tillery and A B Schwartz ldquoDirectcortical control of 3D neuroprosthetic devicesrdquo Sciencevol 296 no 5574 pp 1829ndash1832 2002

[2] H Wang and A Bezerianos ldquoBrain-controlled wheelchaircontrolled by sustained and brief motor imagery BCIsrdquoElectronics Letters vol 53 no 17 pp 1178ndash1180 2017

[3] B Rebsamen C Cuntai Guan H Zhang et al ldquoA braincontrolled wheelchair to navigate in familiar environmentsrdquo

1 2 3 4 5 6 7 Avg

Accuracy comparison chart

82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg

ITR comparison chart

(b)

02468

10121416

1 2 3 4 5 6 7 Avg

Variance comparison chart

(c)

Figure 8 Experimental results of our proposed BCI system for evaluating the classification accuracies ITR and variance value amongsubjects (a) e classification accuracy for each subject (b) Information transfer rate for each subject (c) Variance value for each subject


IEEE Transactions on Neural Systems and RehabilitationEngineering vol 18 no 6 pp 590ndash598 2010

[4] A N Belkacem S Saetia K Zintus-art et al ldquoReal-timecontrol of a video game using eye movements and twotemporal EEG sensorsrdquo Computational Intelligence andNeuroscience vol 2015 Article ID 653639 10 pages 2015

[5] K Sumi K Yabuki T J Tiam-Lee et al ldquoA cooperative gameusing the P300 EEG-based brain-computer interfacerdquoAssistive and Rehabilitation Engineering 2019

[6] A N Belkacem S Nishio T Suzuki H Ishiguro andM Hirata ldquoNeuromagnetic decoding of simultaneous bilat-eral hand movements for multidimensional brainndashmachineinterfacesrdquo IEEE Transactions on Neural Systems and Reha-bilitation Engineering vol 26 no 6 pp 1301ndash1310 2018

[7] Q Gao L Dou A N Belkacem and C Chen ldquoNoninvasiveelectroencephalogram based control of a robotic arm forwriting task using hybrid BCI systemrdquo BioMed ResearchInternational vol 2017 Article ID 8316485 8 pages 2017

[8] W He Y Zhao H Tang C Sun and W Fu ldquoA wireless BCIand BMI system for wearable robotsrdquo IEEE Transactions onSystems Man and Cybernetics Systems vol 46 no 7pp 936ndash946 2016

[9] V Gilja P Nuyujukian C A Chestek et al ldquoA high-performanceneural prosthesis enabled by control algorithm designrdquo NatureNeuroscience vol 15 no 12 pp 1752ndash1757 2012

[10] I Seanez-Gonzalez C Pierella A Farshchiansadegh et alldquoStatic versus dynamic decoding algorithms in a non-invasivebodyndashmachine interfacerdquo IEEE Transactions on Neural Sys-tems and Rehabilitation Engineering vol 25 no 7 pp 893ndash905 2017

[11] K Watanabe H Tanaka K Takahashi Y NiimuraK Watanabe and Y Kurihara ldquoNIRS-based languagelearning BCI systemrdquo IEEE Sensors Journal vol 16 no 8pp 2726ndash2734 2016

[12] C Chen J Zhang A N Belkacem et al ldquoG-causality brainconnectivity differences of finger movements between motorexecution and motor imageryrdquo Journal of Healthcare Engi-neering vol 2019 Article ID 5068283 12 pages 2019

[13] A N Belkacem H Ishiguro S Nishio M Hirata andT Suzuki ldquoReal-time MEG-based brain-geminoid controlusing single-trial SVM classificationrdquo in Proceedings of the2018 3rd International Conference on Advanced Robotics andMechatronics (ICARM) pp 679ndash684 Singapore July 2018

[14] A N Belkacem S Nishio T Suzuki H Ishiguro andM Hirata ldquoNeuromagnetic geminoid control by BCI basedon four bilateral hand movementsrdquo in Proceedings of the 2018IEEE International Conference on Systems Man and Cyber-netics (SMC) pp 524ndash527 Miyazaki Japan October 2018

[15] L He D Hu M Wan Y Wen K M von Deneen andM Zhou ldquoCommon bayesian Network for classification ofEEG-basedmulticlass motor imagery BCIrdquo IEEE Transactionson Systems Man and Cybernetics Systems vol 46 no 6pp 843ndash854 2016

[16] V Mishuhina and X Jiang ldquoFeature weighting and regula-rization of common spatial patterns in EEG-based motorimagery BCIrdquo IEEE Signal Processing Letters vol 25 no 6pp 783ndash787 2018

[17] B O Mainsah K D Morton L M Collins E W Sellers andC S rockmorton ldquoMoving away from error-related po-tentials to achieve spelling correction in P300 spellersrdquo IEEETransactions on Neural Systems and Rehabilitation Engi-neering vol 23 no 5 pp 737ndash743 2015

[18] V Martinez-Cagigal J Gomez-Pilar D Alvarez andR Hornero ldquoAn asynchronous P300-based brainndashcomputer

interface web browser for severely disabled peoplerdquo IEEETransactions on Neural Systems and Rehabilitation Engi-neering vol 25 no 8 pp 1332ndash1342 2017

[19] Y Wang R Wang X Gao B Hong and S Gao ldquoA practicalVEP-based brainndashcomputer interfacerdquo IEEE Transactions onNeural Systems and Rehabilitation Engineering vol 14 no 2pp 234ndash240 2006

[20] P-L Lee C-L Yeh J Y-S Cheng C-Y Yang and G-Y LanldquoAn SSVEP-based BCI using high duty-cycle visual flickerrdquoIEEE Transactions on Biomedical Engineering vol 58 no 12pp 3350ndash3359 2011

[21] R Z T Camilleri O Falzon and K P Camiller ldquoTo train ornot to train A survey on training of feature extractionmethods for SSVEP-based BCIsrdquo Journal of Neural Engi-neering vol 15 no 5 p 1741 2018

[22] K-K Shyu Y-J Chiu P-L Lee J-M Liang and S-H PengldquoAdaptive SSVEP-based BCI system with frequency and pulseduty-cycle stimuli tuning designrdquo IEEE Transactions onNeural Systems and Rehabilitation Engineering vol 21 no 5pp 697ndash703 2013

[23] D Regan ldquoElectrical responses evoked from the humanbrainrdquo Scientific American vol 241 no 6 pp 134ndash146 1979

[24] A Guneysu andH L Akin ldquoAn SSVEP based BCI to control ahumanoid robot by using portable EEG devicerdquo in Pro-ceedings of the 2013 35th Annual International Conference ofthe IEEE Engineering in Medicine and Biology Societypp 6905ndash6908 IEEE Osaka Japan July 2013

[25] C Chen X Li A N Belkacem et al ldquoe mixed kernelfunction SVM-based point cloud classificationrdquo InternationalJournal of Precision Engineering and Manufacturing vol 20no 5 pp 737ndash747 2019

[26] H Nezamfar S S M Salehi M Moghadamfalahi andD Erdogmus ldquoFlashTypetrade a context-aware c-VEP-basedBCI typing interface using EEG signalsrdquo IEEE Journal ofSelected Topics in Signal Processing vol 10 no 5 pp 932ndash9412016

[27] Y Li J Pan F Wang and Z Yu ldquoA hybrid BCI systemcombining P300 and SSVEP and its application to wheelchaircontrolrdquo IEEE Transactions on Biomedical Engineeringvol 60 no 11 pp 3156ndash3166 2013

[28] E Yin Z Zhou J Jiang Y Yang and D Hu ldquoA dynamicallyoptimized SSVEP brainndashcomputer interface (BCI) spellerrdquoIEEE Transactions on Biomedical Engineering vol 62 no 6pp 1447ndash1456 2015

[29] A Abdullah J S Norton and T Bretl ldquoAn SSVEP-basedbrainndashcomputer interface for text spelling with adaptivequeries that maximize information gain ratesrdquo IEEE Trans-actions on Neural Systems and Rehabilitation Engineeringvol 23 no 5 pp 857ndash866 2015

[30] P-L Lee H-C Chang T-Y Hsieh H-T Deng andC-W Sun ldquoA brain-wave-actuated small robot car usingensemble empirical mode decomposition-based approachrdquoIIEEE Transactions on Systems Man and Cybernetics-Part ASystems and Humans vol 42 no 5 pp 1053ndash1064 2012

[31] Y Lu and L Bi ldquoEEG signals-based longitudinal controlsystem for a brain-ControlledVehiclerdquo IEEE Transactions onNeural Systems and Rehabilitation Engineering vol 27 no 2pp 323ndash332 2019

[32] Z Lin C Zhang WWu and X Gao ldquoFrequency recognitionbased on canonical correlation analysis for SSVEP-basedBCIsrdquo IEEE Transactions on Biomedical Engineering vol 53no 12 pp 2610ndash2614 2006

[33] W Nan C M Wong B Wang et al ldquoA comparison ofminimum energy combination and canonical correlation


analysis for SSVEP detectionrdquo in Proceedings of the 2011 5thInternational IEEEEMBS Conference on Neural Engineeringpp 469ndash472 Cancun Mexico April 2011

[34] M Nakanishi Y Wang Y-T Wang Y Mitsukura andT-P Jung ldquoEnhancing unsupervised canonical correlationanalysis-based frequency detection of SSVEPs by incorpo-rating background EEGrdquo in Proceedings of the 2014 36thAnnual International Conference of the IEEE Engineering inMedicine and Biology Society pp 3037ndash3040 IEEE ChicagoIL USA August 2014

[35] P P Banik M K Azam C Mondal and M A RahmanldquoSingle channel electrooculography based Human-ComputerInterface for physically disabled personrdquo in Proceedings of theInternational Conference on Electrical Engineering amp Infor-mation Communication Technology Dhaka Bangladesh May2015

[36] P Lukas N Iveta and C Martin ldquoMake life easier based ondetection of eyes movements laboratory measurementrdquo IFACPapersOnLine vol 48 no 4 pp 268ndash271 2015

[37] N Sarkar B OrsquoHanlon A Rohani et al ldquoA resonant eye-tracking microsystem for velocity estimation of saccades andfoveated renderingrdquo in Proceedings of the IEEE InternationalConference on Micro Electro Mechanical Systems Las VegasNV USA January 2017

[38] Y Tanji M Nakanishi K Suefusa and T Tanaka ldquoWave-form-based multi-stimulus coding for brain-computer in-terfaces based on steady-state visual evoked potentialsrdquo inProceedings of the 2018 IEEE International Conference onAcoustics Speech and Signal Processing (ICASSP) pp 821ndash825 IEEE Calgary Canada April 2018

[39] E Dong C Li L Li et al ldquoClassification of multi-class motorimagery with a novel hierarchical SVM algorithm for braincomputer interfacesrdquo Medical amp Biological Engineering ampComputing vol 55 no 10 pp 1809ndash1818 2017


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AerospaceEngineeringHindawiwwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

damaged muscle and nervous system and complete the dailywork independently is has caused serious physical andmental trauma and their lives are very painful which willaffect their recovery process to some extent How to restoreor enhance their control and communication capabilities tothe outside world has been the goal that has been pursued formany years in the field of medical rehabilitation ereforeBCIs can be used for helping patients with severe braindisorders or muscle damages to regain their ability tocommunicate directly with the outside environmentthrough the brain electrophysiology response [1ndash3] BCI canalso be beneficial for the elderly as advanced assistive andrehabilitative technologies and useful for young able-bodiedfor controlling video games for entertainment [4 5] orcontrolling a robotic arm for several purposes [6ndash9]However most of the traditional brain-computer interfaceequipment is expensive bulky and tedious which makes itdifficult to popularize and apply brain-computer interfacetechnology in real life e portable brain-computer inter-face has become one of the hotspots in the field of the brain-computer interface because of its advantages of easy to carryeasy to use safe and reliable

BCI technology is mainly divided into two types ofbrain activity measurement invasive BCI and noninvasiveBCI depending on the way of putting the electrodes torecord the electrical brain activity [10ndash14] Among themthe invasive BCI might lead to an immune reaction whichcauses serious harm to the user and it is hardly accepted bydisabled people because of the invasiveness of the tech-nique which requires a dedicated surgery and its cost withequipment is very expensive and not covered by manygovernments yet Although the noninvasive brain-com-puter interface is less accurate than the invasive BCI it isstill relatively cheaper compared with all other techniquesand everyone can easily accept it ere are several para-digms to control machine or computer using our brainsignal characteristics and the most popular ones are motorimagery [15 16] P300 wave [17 18] steady state visualevoked potentials (SSVEP) [19ndash21] for building practicalbrain-computer interface systems So far the SSVEPmethod was applied widely because of the high signal-to-noise ratio and robustness [22] SSVEP induction meansthat when the human brain receives the stimulation of afixed frequency scintillation block an uninterrupted re-sponse related to the stimulation frequency will be gen-erated in the visual cortex of the human brain is SSVEPbrain response is a very useful natural involuntary phe-nomenon which has been tested by researchers many timese earliest SSVEP-BCI system designed by Regan in1979 allowed subjects to select a flashing button on thecomputer screen by simply looking at the computer screen[23] basically achieving the desired design goals enMullerputz and Guneysu and Akin applied the SSVEP-BCIsystem to the physical control of neural limb and humanoidrobot respectively and achieved good control results [24]In this paper we chose SSVEP because it does not need anytraining phase for subjects and has very high accuracycompared with P300 or motor imagery using single trialelectroencephalography (EEG) signal e commonly used

signal processing and classification methods of SSVEPinclude fast Fourier transform (FFT) wavelet transformcanonical correlation analysis (CCA) linear discriminantanalysis (LDA) and support vector machine (SVM) In thispaper CCA was used for developing our signal processingalgorithm Compared with other SSVEP signal classifica-tion algorithms [10ndash14 25] CCA classification algorithm isfast efficient simple and easy to use

In some previous researches the SSVEP paradigm wassuccessfully used in writing tasks [26] In the paper [27] wecan see that the authors proposed a hybrid brain-computerinterface system that combines P300 and SSVEP modalitiesis combined system has improved the accuracy of EEG-based wheelchair control In addition SSVEP has been alsoused in the mental spelling system [28 29] In the paper [30]the authors used three flash speeds to control the small robotcar Lee et al only use OZ as the reference electrode to collectand process EEG signals In the paper [31] Lu and Bi haveproposed a longitudinal control system for brain-controlledvehicles based on EEG signals However it is still unknownwhether it can be used in the industry

In this paper a new type of intelligent crawler robot isdesigned for cleaning the walls which is considered as one ofthe smart home appliances Compared with the wheeledrobot [32] the crawler robot has the advantages of long lifeand high carrying capacitye intelligent crawling robot forthe walls used in this experiment adds an adsorption deviceusing negative vacuum pressure which effectively solves theproblem of sliding of the cleaning robot on a wall with acertain inclination angle e BCI based on SSVEP canusually provide a high information transmission rate theverification process of the system is relatively simple andno training of the subjects is required However becausethe SSVEP of some subjects is very weak and vulnerable tothe interference of other noise signals how to accuratelyidentify SSVEP from a short time window is still a chal-lenging problem in BCI research based on SSVEP is isalso the subject that we will continue to study in the futureIn this study the SSVEP paradigm was designed to controlthe crawler robot for cleaning the dust on the walls Weused the high accuracy SSVEP paradigm to cooperate withour cleaning robot to complete the designed experimentTo our best knowledge this is the first report which usedbrain machine interface for crawling cleaning robot controlto help persons with disabilities to improve their quality oflife

is paper is arranged as follows in the Materials andMethods section the experimental paradigm and analysismethod of brain signal and the motion model of the in-telligent crawling robot were introduced At the same timethe offline experiment and online experiment are completedand the data analysis is carried out In the Results section theoffline and online experiments were summarized and dis-cussed separately and the accuracy and ITR of the exper-iment were obtained Our experiments validate our viewsand achieve the desired results In the Discussion part wemainly talk about the limitations of the system and putforward the future changes Finally conclusion and pros-pects of future work are given in Section 5











Serial port

Bluetooth

Signal acquisition

Sign

al p

roce

ssin

g











EOG

EOG

P3 Pz P4P8

Fp2Fp1

F7

T7 C3

FC1

F3

10cm red

FzF4

F8

Ref FC2 FC6

Cz C4 T8

CP5CP1 CP2

CP6TP10

O1 Oz

TP9

P7

FC5

O2

Gnd30cm green











O2 P8)







Yes

No

Task

Idle


Signal processing


Subject


location ofthe dust

Decide onthe




Yes

NoNo

Yes





Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn


sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)



maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT


1113969

(2)



ITR 60t


1113890 1113891

(3)






x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)


θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)


3 Results








4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia











Serial port

Bluetooth

Signal acquisition

Sign

al p

roce

ssin

g











EOG

EOG

P3 Pz P4P8

Fp2Fp1

F7

T7 C3

FC1

F3

10cm red

FzF4

F8

Ref FC2 FC6

Cz C4 T8

CP5CP1 CP2

CP6TP10

O1 Oz

TP9

P7

FC5

O2

Gnd30cm green











O2 P8)







Yes

No

Task

Idle


Signal processing


Subject


location ofthe dust

Decide onthe




Yes

NoNo

Yes





Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn


sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)



maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT


1113969

(2)



ITR 60t


1113890 1113891

(3)






x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)


θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)


3 Results








4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









EOG

EOG

P3 Pz P4P8

Fp2Fp1

F7

T7 C3

FC1

F3

10cm red

FzF4

F8

Ref FC2 FC6

Cz C4 T8

CP5CP1 CP2

CP6TP10

O1 Oz

TP9

P7

FC5

O2

Gnd30cm green











O2 P8)







Yes

No

Task

Idle


Signal processing


Subject


location ofthe dust

Decide onthe




Yes

NoNo

Yes





Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn


sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)



maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT


1113969

(2)



ITR 60t


1113890 1113891

(3)






x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)


θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)


3 Results








4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia










O2 P8)







Yes

No

Task

Idle


Signal processing


Subject


location ofthe dust

Decide onthe




Yes

NoNo

Yes





Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn


sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)



maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT


1113969

(2)



ITR 60t


1113890 1113891

(3)






x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)


θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)


3 Results








4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




Y(f)

sin 2 πfn

cos 2 πfn

sin 4 πfn

cos 4 πfn


sin 2Nhπfn

cos 2Nhπfn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

n 1fs

2fs

N

fs

(1)



maxWxWy

ρ(x y) E xTy1113858 1113859

E xTx1113858 1113859

1113969E yTy1113858 1113859

E WT

xXYTWy1113960 1113961E WT


1113969

(2)



ITR 60t


1113890 1113891

(3)






x(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859cos[θ(t)]dt 0

y(t) 12

1113946t

0vL(t) + vR(t)1113858 1113859sin[θ(t)]dt 0

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(4)


θ(t) 1D

1113946t

0vR(t) minus vL(t)1113858 1113859dt

2vR

Dt (5)


3 Results








4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






4 Discussion



Yf1

Yf2

Yfk

CCA

CCA

CCA

MAX

ρ 1

ρ 2

ρ k

BCIcommand

Refer signal Yf

hellip hellip

hellip


X

Y

OOR0

OR1

YR0

XR0

YR1 XR1
















5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia














5 Conclusion




Data Availability




Acknowledgments


References




1 2 3 4 5 6 7 Avg


82

84

86

88

90

92

94

(a)

195

20

205

21

215

22

225

23

1 2 3 4 5 6 7 Avg


(b)

02468

10121416

1 2 3 4 5 6 7 Avg


(c)














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia













































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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