CLASSIFICATION OF MOTOR IMAGINARY TASKS USING

ADAPTIVE RECURSIVE BANDPASS FILTER

Effective Classification for Motor Imaginary BCI

Vickneswaran Jeyabalan, Andrews Samraj

Faculty of Engineering and Technology,Faculty of Information Science and Technology, Multimedia University

Jalan Ayer Keroh Lama, Melaka, Malaysia

Loo Chu Kiong

Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia

Keywords: Brain Computer Interface, Motor imagination, mu rhythm, adaptive filtering.

Abstract: The noteworthy point in the advancement of Brain Computer Interface (BCI) research is not only to develop

a new technology but also to adopt the easiest procedures since the expected beneficiaries are of disabled.

The nature of the locked-in patients is that, they possess strong mental ability in thinking and understanding

but they are extremely unable to express their views. Imagination is possible for almost all of the locked-in

patients; hence a BCI which does not rely on finger movements or other muscle activity is definitely an

added advantage in this arena. The objective of this paper is to identify and classify motor imaginary signals

extracted from the left and right cortex of the human brain. This is realised by implementing an adaptive

bandpass filter with the combination of frequency shifting and segmentation techniques. The signals are

captured using Electro-Encephalogram (EEG) from the C3, C4, and Cz channels of the scalp electrodes and

is pre-processed to expose the motor imaginary signals. The result of classification using a simple threshold

articulates the effectiveness of our proposed technique. The best results were found in the latency range of 3

to 9 seconds of the imagination and this proves the existing neuro-science knowledge.

1 INTRODUCTION

Brain Computer Interface (BCI) or also know, as

Brain Machine Interface (BMI) is a communication

interface between the brain and machine. The main

purpose of a BCI is to provide a new mode of

communication for people who have severe motor

disabilities but being cognitively intact. (Baharan et

al., 2005). The input, which is the brain signal, for a

BCI system can be obtained invasively or non-

invasively. The method of obtaining brain signals by

electrode implantation is knows as an invasive

approach. This method is frequently described as

dangerous because it involves surgical procedures to

implant the electrodes in the brain. Whereas, brain

signals taken from the electrodes placed on the

surface of the scalp is categorised as a non-invasive

method. This method can be performed by using the

Electro-Encephalogram (EEG). Various types of

signals are used as an input for the EEG based BCI.

For example, Visual Evoked Potential (Andrews et

al., 2005; Andrews et al., 2007), Mu-rhythm

(Coyle et al, 2005) and P300 (Donchin et al, 2000).

In this paper we concentrate on the motor

imagination signals which co-relates with the mu-

rhythm.

The human brain’s sensory motor cortex shows

the rhythmic activities of physical motor

movements. This can also be observed during motor

imagination. (Pfurtscheller and Neuper, 2003). This

rhythmic activity can be observed in the Alpha

frequency band of an EEG signal, which is in the

frequency range of 8Hz to 12Hz. Mu rhythm can be

best measured from the EEG channel C3, Cz and

C4.

Imagination of motor movements results in the

power attenuation of the EEG signal and can be

observed as features. The attenuation of the power is

known as event related de-synchronisation (ERD)

whereas the rebounding of the power is known as

113

Jeyabalan V., Samraj A. and Chu Kiong L. (2008).

CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classiﬁcation for Motor Imaginary

BCI.

In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 113-118

DOI: 10.5220/0001935501130118

 SciTePress

event related synchronisation (ERS). The ERD or

ERS occurs contra laterally to the intended

movements. For example, for right motor

movements, the ERD is observed at the left

hemisphere of the brain and for the left motor

movements, the ERD is found at the right

hemisphere. Classifications of right or left motor

movements are usually made based on the ERD of

the signal.

In a normal ERD detection procedure (Kalcher

and Pfurtscheller, 1995), EEG signals are usually

filtered in a narrow band, squared, low pass filtered

and averaged over trial. However, it is

disadvantageous to use this method because useful

information will be lost from the averaging over

multi-trials.

In this paper we have implemented the adaptive

recursive bandpass filter (Gharieb and Cichocki,

2001) to detect imaginary motor movements and to

classify them according to left or right movements

without observing the ERD and averaging over multi

trials. Implementation of this method reduces the

chances of information lost and increases the

classification rate. This method has been

successfully implemented on the BCI Competition

2003 dataset IIIb, which consists of 140 labelled and

unlabelled trials respectively, further information of

the data set, is explained in the next section.

The target signal, which contains mu rhythm, is

first pre-processed by implementing a band pass

filter. Then, the adaptive recursive bandpass filter is

used to estimate the dominant signal, which

represents the motor movements. The employed

adaptive recursive filter is used to trail the centre

frequency of the dominant EEG signal. The filter

requires only one coefficient to be updated in order

to adjust the centre frequency of the filter bandpass

to be approximated with that of the input signal

(Gharieb and Cichocki, 2001). The time function of

the coefficient represents the distinct feature for each

signal and represents either left or right imaginary

motor movements.

2 METHODOLOGY

2.1 Data Set

This method was trialed on the BCI Competition

2003, dataset IIIb (BCI Competition II, 2003). The data

set was provided by the Department of Medical

Informatics, Institute for Biomedical Engineering,

University of Graz. The signals were obtained from

a 25-year-old female relaxing on a chair with

armrest. The task was to control a feedback bar by

means of imagining left hand of right hand

movements. The data was acquired from the EEG

channels C3, Cz, and C4 (figure 1), which was band

pass filtered for a frequency range of 0.5 to 30Hz

and sampled at 128 Hz. The experiment consists of 7

runs with 40 trials each. All runs were conducted on

the same day with several minutes break in between.

The data has a total of 280 trials, which consists of

140 labelled and 140 unlabeled trials with an equal

number of left hand and right hand movements.

Each trial consists of duration of 9 seconds. At the

second a visual cue, an arrow pointing left or

right is presented to indicate left or right motor

movements is to be imagined.

321C3 Cz C4

5 cm

Figure 1: Electrode positions.

0123456789

sec

Trigger

Beep

Feedback period with Cue

Figure 2: The timing scheme.

2.2 Signal Analysis

The trials were divided into two groups according to

right or left motor imaginary. Signals from channel

C3 and C4 of each group are first pre-processed by

means of band pass filtering. A band pass filter

using 7

order Butterworth filter where the pass

band is 9Hz with less than 1 dB of ripple and the

stop band is 11Hz with at least 6 dB of attenuation.

Signals from channel Cz is ignored because it

contains very little significant discriminative

features (Lemm et al., 2004). After band passing the

signals, we could observe that the signal is densely

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populated by the main receptive frequencies, which

are the range of 8 Hz to 10Hz at the 3

to 9

seconds. This is because the motor imaginary task

begins after the que at the 3

second.

Then the adaptive recursive bandpass filtering

(Gharieb and Cichocki, 2001) approach is applied to

the signals between the ranges of 3

to 9

seconds

of each channel. This approach is employed to

estimate and track the centre frequency of the

dominant signal of each channel. The main

advantage of this approach is that the adaptive filter

updates only one coefficient (Gharieb and Cichocki,

2001). The coefficient is updated in order to adjust

the centre frequency of the filter band pass to be

matched with the input data (Gharieb and Cichocki,

2001). The time function of the coefficient

represents the features for each signal, which is

feasible to be used for classification. A fourth order

Butterworth band pass filter is employed as the

adaptive filter. The filter function, T (z) could be

expressed as (Raja Kumar et al., 1985; Raja Kumar

et al., 1990).

T(z)=

02 4

12 2 3 4

1 () ( () ') ()

12234

DDZ DZ

FC n Z F C n F Z FC n Z F Z

−−

−−−−

++++ +

where

1/( 2 1),

DD l l== ++

DD=−

13 0

DD D==− 2(2 2) ,

ll D=− +

lD=

'2( 1),

lD=−

2( 2 2) ,

ll D=−+

(21),

llD=−+

cot ( )lanBP

(1)

The coefficient C (n) could be expressed as

cos( ( ( ) ( ))

()

cos( )

Hn Hn

where

()

Hn= normalised low cut off frequency,

()

Hn= normalised high cut off frequency,

P = normalised bandwidth of the filter

(2)

P is assumed to be a constant value. Based on

equation 1 and 2, it can be seen that

()Cn

is the only

coefficient that has to be updated by the adaptive

filter since it is also the only coefficient which is

dependant with the centre frequency,

(

()

Hn+ ()

Hn)/2. Hence the filter has only one

centre frequency dependent,

()Cn to be updated. In

order for the filter, T (z) to be self-adjusted to the

centre frequency of the input signal, the output

power of the filter should be maximised (Raja

Kumar et al., 1985; Raja Kumar et al., 1990). The

adaptive coefficient is updated for the maximization

of the expected output power (Gharieb and

Cichocki, 2001). This step can be applied by

implementing a standard gradient approach (Gharieb

and Cichocki, 2001). An algorithm called recursive

maximum mean-squared (RMXMS) is used to

update the filter coefficient (Gharieb and Cichocki,

2001). The adaptive filter further enhances the

feature and provides good on-line information of the

feature’s distinct behaviour.

The adaptive filter becomes unstable for some

low frequency waves. This is because the adaptive

filter is a band pass filter and the EEG signal is a

low pass signal (Gharieb and Cichocki, 2001). So, in

order to solve this problem a high frequency shifting

process is employed to shift the EEG frequency to

the highest ones before the adaptive filtering

(Gharieb and Cichocki, 2001). The updated co

efficient is the used as the features that represents

the signals resulting from right or left motor

imaginary. Figure 3 shows the method applied

graphically. Figure 4 shows the extracted features

after implementing the proposed method for an

average of ten trials.

Figure 3: Signal analysis methodology.

Band pass 7

order Butterworth

filter

Input

EEG signals from channel.

C3 and C4

Adaptive Band pass filter. (4

order Butterworth filter)

Distance Based Classification

Filter co-efficient, C(n)

Pre Processed signal

CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER -

Effective Classification for Motor Imaginary BCI

115

Figure 4: 10 Realisation of raw motor imaginary signals

(Top). Mean values of the extracted features after

performing adaptive recursive band pass filtering

(bottom).

As for the classification, features from both C3 and

C4 in a trial are taken. The sum values of the

features during right and left imaginaries for each

channel are taken respectively. The sum values

represent the feature sequences. The distance of the

features from C3, which is less than the features

from C4, is regarded as right hand movements.

Whereas, the distance of features from C3, which is

more than the features from C4, is regarded as left-

hand movements. Clear distinction between left and

right motor imaginary could be observed in the

features taken from the beginning of the imagination

period.

Table 1: Classification of motor imaginary respective to

the imagination period using ERD detection.

Time

Right hand

imaginary

Left hand

imaginary

Start (s) End (s)

3 9 73% 71%

3 6 76% 61%

3 5 63% 51%

3 4 56% 47%

1 3 44% 53%

Table 2: Classification of motor imaginary respective to

the imagination period using adaptive recursive b/pass

filter.

Time

Right hand

imaginary

Left hand

imaginary

Start (s) End(s)

3 9 100% 100%

3 6 100% 98%

3 5 91% 94%

3 4 61% 90%

1 3 70% 17%

3 RESULTS AND DISCUSSIONS

Experiments were conducted using the ERD

detection method (Kalcher and Pfurtscheller, 1995)

and also the adaptive recursive band pass filter. The

classification results for both of the methods are

shown in Table 1 and Table 2. It can be clearly

observed that the classification results using the

adaptive recursive band pass filter method

outperforms the conventional ERD detection

method. The classification accuracy varies according

to the time of when the features are taken. Referring

to Table 2, the best classification accuracy could be

seen from the time range of 3

to 9

seconds.

The results are also depicted in figures 5-10. Out

of the 140 labelled trials, 70 are with right hand

movement imaginaries and 70 are with left hand

movement imaginaries. Four sets of figures are

shown for each movement imaginaries where the

features are averaged over 15, 30 and 70 trials. It can

be seen that there are clear visual distinctions of the

features from channels C3 and C4 resulted contra

laterally from the hand movements.

Figure 5: Average 15 right hand trials. Dark line shows C4

features. Dotted line shows C3 features.

Figure 6: Average 30 right hand trials. Dark line shows C4

features. Dotted line shows C3 features.

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0 100 200 300 400 500 600 700 800

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0.05

Figure 7: Average70 right hand trials. Dark line shows C4

features. Dotted line shows C3 features.

Figure 8: Average 15 left hand trials. Dark line shows C4

features. Dotted line shows C3 features.

Figure 9: Average 30 left hand trials. Dark line shows C4

features. Dotted line shows C3 features.

0 100 200 300 400 500 600 700 800

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0.05

Figure 10: Average 70 left hand trials. Dark line shows C4

features. Dotted line shows C3 features.

4 CONCLUSIONS

From these results we could see that the features

from the motor imaginary signals extracted using

band pass filtering and adaptive filtering could be

discriminated easily. This method enables us to

obtain better classification results in comparison

with the conventional ERD detection methods.

Better classification results are obtained because;

this method eliminates the averaging over multiple

trials step, which causes useful features to be loss.

This technique could be also used to isolate other

rhythmic component in a signal. Although the

conventional way of bandpass filtering of a signal is

still feasible, this technique can be extended in

identifying useful sources and components in a

single trial recorded EEG signal.

ACKNOWLEDGEMENTS

We would like to acknowledge Department of

Medical Informatics, Institute for Biomedical

Engineering, University of Graz for making the data

available.

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Effective Classification for Motor Imaginary BCI

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