Brain-Computer Interface System Using

Approximate Entropy and EMD Techniques

Qiwei Shi1, Wei Zhou1, Jianting Cao1,2,3,Toshihisa Tanaka2,4, and Rubin Wang3

1 Saitama Institute of Technology1690 Fusaiji, Fukaya-shi, Saitama 369-0293, Japan

2 Brain Science Institute, RIKEN2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan

3 East China University of Science and TechnologyMeilong Road 130, Shanghai 200237, China

4 Tokyo University of Agriculture and Technology2-24-16, Nakacho, Koganei-shi, Tokyo 184-8588, Japan

cao@sit.ac.jp

Abstract. Brain-computer interface (BCI) is a technology which wouldenable us to communicate with external world via brain activities. Theelectroencephalography (EEG) now is one of the non-invasive approachesand has been widely studied for the brain computer interface. In this pa-per, we present a motor imaginary based BCI system. The subject’sEEG data recorded during left and right wrist motor imagery is usedas the input signal of BCI system. It is known that motor imagery at-tenuates EEG μ and β rhythms over contralateral sensorimotor cortices.Through offline analysis of the collected data, a approximate entropy(ApEn) based complexity measure is first applied to analyze the com-plexity between two channels located in different hemispheres. Then, em-pirical mode decomposition (EMD) is used to extract informative brainactivity features to discriminate left and right wrist motor imagery tasks.The satisfactory results we obtained suggest that the proposed methodhas the potential for the classification of mental tasks in brain-computerinterface system.

Keywords: Brain-computer Interface (BCI), Motor Imagery, Electroen-cephalography (EEG), Approximate Entropy (ApEn), Empirical ModeDecomposition (EMD).

1 Introduction

Brain-computer interface (BCI) is a system that uses electric, magnetic, or corti-cal neuronal activity signals rather than peripheral nerves and muscles to controlexternal devices such as computers, switches, wheelchairs. Like any communica-tion or control system, a BCI system has input (e.g., electrophysiological activityfrom the object), output (e.g., device commands), components that translate in-put into output, and a protocol that determines the onset, offset, and timing ofoperation [1].

Y. Tan, Y. Shi, and K.C. Tan (Eds.): ICSI 2010, Part II, LNCS 6146, pp. 204–212, 2010.c© Springer-Verlag Berlin Heidelberg 2010

BCI System Using Approximate Entropy and EMD Techniques 205

The most exploited signal in BCI is the scalp-recorded electroencephalogram(EEG) which is a noninvasive measurement of the brain’s electrical activitiesand has a temporal resolution of milliseconds. The most existing BCI systemsuse three basic signal-proceeding blocks [2]. The system applies a preprocessingstep to remove noise and artifacts which mostly related to ocular, muscular andcardiac. In the next step, the system perform feature extraction and selectionto detect the specific target patterns in brain activity that encode the user’smental tasks or motor intentions. The last step is aimed at translating thesespecific features into useful control signals to be sent to an external device [9].

Recently, Brain-computer Interface (BCI) research has been evolved tremen-dously. BCI provides control capabilities to people with motor disabilities. Thereare many experimental approaches including P300, VEP (Visual Evoked Poten-tial), SSVEP (Steady State Visual Evoked Potential), and motor imagery thatcarried out to study BCI system [3,4]. The movement-related BCI aims at pro-viding an alternative non-muscular communication path and control system formotion disabled people to send the command to the external world using themeasures of brain activity. This type of brain-computer interface is based upondetection and classification of the change of EEG rhythms during different motorimagery tasks, such as the imagination of left and right hand movements.

One approach of motor imagery based BCI is to exploit spectral characteris-tics of μ rhythm (8–12 Hz) and β rhythm (12–30 Hz). These oscillation typicallydecrease during, or in preparing for a movement–event related desynchronization(ERD), and increase after movement and in relaxation–event related synchro-nization (ERS) [6]. That is to say, for example, left hand motor imagery makesμ or β rhythm decrease in the sensory motor region of right hemisphere.

This paper describes a method of complexity measure associating with EMDtechnique. Approximate entropy (ApEn) reflects the different complexity of twoelectrodes’ signals. EMD takes its effect in extraction the feature between left andright motor imagery. The experimental results illustrate the proposed method iseffective in the classification of motor imagery EEG.

2 Methods

2.1 The Approximate Entropy

Approximate entropy (ApEn) is a regularity statistic quantifying the unpre-dictability of fluctuations in a time series that appears to have potential ap-plication to a wide variety of physiological and clinical time-series data [7,8].Intuitively, one may reason that the presence of repetitive patterns of fluctua-tion in a time series renders it more predictable than a time series in which suchpatterns are absent.

To computing the ApEn(m, r) (m: length of the series of vectors, r: toler-ance parameter) of a time series {x(k)}, (k = 1, . . . , N), v(k) = [x(k), x(k +1), . . . , x(k + m − 1)] is first constructed from the signal samples {x(k)}. LetD(i, j) denote the distance between two vectors v(i) and v(j) (i, j ≤ N −m+1),

206 Q. Shi et al.

which is defined as the maximum difference in the scalar components of v(i) andv(j), or

D(i, j) = maxl=1,···,m

|vl(i) − vl(j)|. (1)

Then, compute the metric Nm,r(i), which represents the total number of vectorsv(j) whose distance with respect to the generic vector v(j) is less than r, orD(i, j) ≤ r. Now define Cm,r(i), the probability to find a vector that differsfrom v(i) less than the distance r, as follows:

Cm,r(i) =Nm,r(i)

N − m + 1, (2)

φm,r =∑N−m+1

i=1 log Cm,r(i)N − m + 1

. (3)

For m + 1, repeat above steps and compute φm+1,r. ApEn statistic is given by

ApEn(m, r) = φm,r − φm+1,r. (4)

The typical values m = 2 and r between 10% and 25% of the standard deviationof the time series {x(k)} are often used in practice [7].

2.2 Empirical Mode Decomposition

The EMD method as a time-frequency analysis tool for nonlinear and nonsta-tionary signals has been proposed in [5]. EMD is a fully data driven techniquewith which any complicated data set can be decomposed into a finite and oftensmall number of Intrinsic Mode Functions (IMF).

An IMF component as a narrow band signal is a function defined havingthe same numbers of zero-crossing and extrema, and also having symmetricenvelopes defined by the local maxima and minima respectively.

The procedure to obtain the IMF components from an observed signal is calledsifting and it consists of the following steps:

1. Identification of the extrema of an observed signal.2. Generation of the waveform envelopes by connecting local maxima as the

upper envelope, and connection of local minima as the lower envelope.3. Computation of the local mean by averaging the upper and lower envelopes.4. Subtraction of the mean from the data for a primitive value of IMF compo-

nent.5. Repetition above steps, until the first IMF component is obtained.6. Designation the first IMF component from the data, so that the residue

component is obtained.7. Repetition above steps, the residue component contains information about

longer periods which will be further resifted to find additional IMF compo-nents.

BCI System Using Approximate Entropy and EMD Techniques 207

The sifting algorithm is applied to calculate the IMF components based on acriterion by limiting the size of the standard deviation (SD) computed from thetwo consecutive sifting results as

SD =T∑

t=0

[(hk−1 (t) − hk (t))2

h2k−1 (t)

]

. (5)

in which a typical value for SD can be set between 0.2 and 0.3 for the siftingprocedure.

Based on the sifting procedure for one channel of the real-measured EEGdata, we finally obtain

x(t) =n∑

i=1

ci(t) + rn(t). (6)

In Eq. (6), ci(t)(i = 1, · · · , n) represents n IMF components, and rn representsa residual component. The residual component can be either a mean trend or aconstant. Since each IMF component has a specific frequency, it is easily to dis-card high frequency electrical power interference after raw data decomposition.The rest desirable components are combined to a new signal x′(t).

3 Experiments and Results

3.1 Motor Imagery Experiment

In our experiment, the EEG signal was recorded by NEUROSCAN ESI system.As illustrated in Fig. 1(a), six exploring electrodes (F3, F4, C3, C4, P3, P4) areplaced on forehead and two references are placed on earlobes (A1, A2) basedon the standardized 10-20 system. The sampling rate of EEG is 500 Hz and theresistance of each electrode is set to less than 8 kΩ.

EEG data recorded during right or left wrist movement imagery is used tosimulate a BCI input data sources. The subject sat in a relaxing condition andwas presented with a series of sound task by STIM AUDIO SYSTEM, from whichthe subject is able to perform motor imagery and eyes’ closing alternatively. Asshowed in Fig. 1(b), the onset of mental wrist movement is paced with theinterval of 3 seconds. In the first session, the subject attempted to imagineleft wrist movement after the sound signal. In the second session, right wristmovement imagery was carried out. Each of the section lasted about 300 secondsincluding wrist movement imagery 50 times and eyes’ closing 50 times.

As an example, a small time window of eight seconds (79–87 sec.) right wristmovement imagery EGG signal is shown in Fig. 2. Event 1 (i.e., red line) is thesound signal for motor imagery and event 2 (i.e., green line) is the one for eyes’closing. The subject began to imagine the right wrist movement when event 1was presented and stopped as soon as event 2 appeared.

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(a)

motor imagery close eyes

3 sec 3 sec

(b)

Fig. 1. (a) The location of six electrodes and two references (A1, A2) based on thestandardized 10-20 system in the motor imagery experiment. (b) The process of eachwrist mental movement task.

60.03

+-

Scale

1 2 1

79 80 81 82 83 84 85 86 87

F3

F4

C3

C4

P3

P4

Time

Cha

nnel

s

motor imagery eyes closing

Fig. 2. A view of 8 seconds right wrist movement imagery EEG data. Event 1 is themotor imagery stimulation and event 2 is for subject’s eyes’ closing.

3.2 ApEn Results for Wrist Motor Imagery EEG

xIn this subsection, we firstly use ApEn measure to analyze the recorded EEGsignals from channel C3 and C4. These two typical channels are located sepa-rately in two areas where are relative to classifying the type of motor imagery.The ApEn calculated results of some motor imagery time points are showedin Table 1 and Table 2. In left wrist motor imagery process (Table 1), ApEnresults of channel C3 are usually lower than those of C4. We suspect the re-sult implies that EEG signal from channel C3 is more predictable since certainbrain wave rhythms occur in the left hemisphere when the subject acts leftwrist motor imagery. Contrarily, lower ApEn of channel C4 EEG signal in rightwrist motor imagery (Table 2) implies regular rhythms occur in the right hemi-sphere.

BCI System Using Approximate Entropy and EMD Techniques 209

Table 1. ApEn results for left wrist motor imagery in some time points (r=0.25)

Chan.Left wrist motor imagery time points (sec.)

52–53 84–85 91–92 117–118 135–136 238–239 270–271 309–310

C3 0.1599 0.1760 0.1855 0.1966 0.1641 0.1196 0.1056 0.2224

C4 0.2721 0.3048 0.2996 0.2865 0.2822 0.3054 0.1887 0.3339

Table 2. ApEn results for right wrist motor imagery in some time points (r=0.25)

Chan.Right wrist motor imagery time points (sec.)

75–76 94–95 95–96 133–134 145–146 203–204 209–210 274–275

C3 0.1645 0.2361 0.2663 0.2583 0.1848 0.2675 0.1781 0.2576

C4 0.1540 0.1483 0.1630 0.1819 0.1132 0.2224 0.1544 0.2405

3.3 EMD for Wrist Motor Imagery EEG

Basing on the result of ApEn measure, we do further analysis by applying EMDmethod to EEG signal from the channel C3 and C4 during the motor imagerytask. As shown in Fig. 3, the signal from the channel C3 of one left wrist move-ment imagery task from 270 to 271 sec. is selected as an example. By applyingthe EMD method described in Section 2, we obtained four IMF components (C1

to C4) within different frequency from high to low and a residual component (r).Generally in our experiment, the component with such a high frequency like C1

refers to electrical interference from environment and the residual component (r)is also not typical useful component, considering.

Several factors suggest that μ and/or β rhythms could be good signal featuresfor EEG-based communication. Mental imagery of movement or preparation formovement is typically accompanied by a decrease in μ and β rhythms, particu-larly contralateral to the movement. This decrease has been labeled ‘event-relateddesynchronization’ (ERD). Its opposite, rhythm increase, or ‘event-related syn-chronization’ (ERS) occurs in the cortex areas without movement or inrelaxation [6].

270 270.1 270.2 270.3 270.4 270.5 270.6 270.7 270.8 270.9 271

1000

1050

1100

EMD Result for One Second Signal in Channel C3

C3

-5

0

5

C1

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0

10

C4

270 270.1 270.2 270.3 270.4 270.5 270.6 270.7 270.8 270.9 271

1060

1080

1100

r

Time(sec.)

Fig. 3. EMD result for one second (270–271 sec.) signal in channel C3.

210 Q. Shi et al.

270 270.1 270.2 270.3 270.4 270.5 270.6 270.7 270.8 270.9 271

1000

1050

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Time(sec.)

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Time(sec.)

270 270.5 271

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270 270.5 271

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1500

270 270.5 271

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0

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C4

Time(sec.)0 10 20 30 40

0

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(b)

Fig. 4. EMD results for channel C3 and C4 signal from a left wrist movement im-agery task (270–271 sec.). (a) Decomposed IMFs for channel C3 in time and frequencydomains. (b) Decomposed IMFs for channel C4 in time and frequency domains.

145 145.1 145.2 145.3 145.4 145.5 145.6 145.7 145.8 145.9 146

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-50

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Time(sec.)

145 145.5 146

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145 145.5 146

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(b)

Fig. 5. EMD results for channel C3 and C4 signal from a right wrist movement im-agery task (145–146 sec.). (a) Decomposed IMFs for channel C3 in time and frequencydomains. (b) Decomposed IMFs for channel C4 in time and frequency domains.

Therefore, after the EMD processing, the rest three IMF components (C2 toC4 in a dotted line box in Fig. 3) that as desirable ones are displayed in their fre-quency domain by applying the Fast Fourier Transform (FFT) (Fig. 4(a)). Withy-coordinate in the scope from 0 to 40Hz, one component within the frequencythat corresponds to the range of μ rhythm is visualized (the second block inright column of Fig. 4(a)). By applying the EMD method to the EEG of channelC4 obtained from the same motor imagery task, the amplitude of each desirableIMF components this time is in a low range (Fig. 4(b)). Comparing the analysisresults in Fig. 4, it is clear that μ rhythm can be extracted from channel C3rather than channel C4. Without loss of generality, the same process is appliedto the EEG signal of other left wrist movement imagery tasks. Similar resultswe obtained implies the left wrist movement imagery lead to the decrease in

BCI System Using Approximate Entropy and EMD Techniques 211

Fig. 6. Extraction of μ rhythm from channel C3 in left wrist movement imagery aswell as that from channel C4 in right wrist movement imagery

μ rhythm in the right hemisphere. In the session of right wrist motor imagery,comparatively, analysis between channel C3 and C4 demonstrates μ rhythm canbe extracted from channel C4 (Fig. 5). The EMD method shows the appearanceof μ rhythm results in difference of ApEn of each hemisphere and correlation be-tween μ rhythm and motor imagery task obtained from the analysis correspondsto the theoretical fact (Fig. 6).

4 Conclusion

In our study, EEG data recorded in an offline BCI experimental setting presentstwo classes which correspond to the left wrist and the right wrist motor imageries.We suggest the applicability of the frequency spatial pattern approach to clas-sification of motor imagery EEG. The approximate entropy (ApEn) is appliedto do preliminary measure of complexity difference between the channel whichdominate the motor imagery. EMD method is used to classify the subject’s mo-tor imagery conditions based on spectral analysis of μ rhythms. Since dependingon the part of the body imagined moving, the amplitude of multichannel EEGrecordings exhibits differences in spatial patterns, we note the proposed methodshowed its effectiveness.

Acknowledgments. This work was supported in part by KAKENHI (22560425,21360179).

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