Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

Kais Belwaﬁ

1,3

, Ridha Djemal

, Fakhreddine Ghaffari

, Olivier Romain

, Bouraoui Ouni

and Soﬁen Gannouni

ENISo of Sousse, University of Sousse, Erriyadh 4023, Sousse, Tunisia

Electrical Engineering, King Saud University, Riyadh, Saudi Arabia

ETIS Laboratory-UMR8051, University of Cergy-Pontoise ENSEA, 6 avenue du Ponceau 95014 Cergy-Pontoise, France

College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Keywords:

Brain Computer Interface (BCI), EEG Filters, Motor Imagery (MI).

Abstract:

Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities

when controlling home devices. However, the development of such EEG-based control system requires a

great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual

variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we

propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach

integrates adaptive ﬁltering techniques customized for each subject during the training phase to increase the

accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data

sets provided by BCI-competition and then using our own on-line validation platform experienced with four

subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and

β bands. These features are classiﬁed by the Linear Discriminant Analysis (LDA) algorithm. Our prototype

integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we

integrated all EEG signal processing including the adaptive ﬁltering. The proposed system achieves 80.5% of

classiﬁcation accuracy, which makes approach a promising method to control an external devices based on the

thought of LH and RH movement.

1 INTRODUCTION

Today, the number of patients suffering from Cere-

broVascular Accident (CVA), Spinal Cord Injury

(SCI) and other similar illnesses is growing ev-

ery day. Improving the life quality of these per-

sons is one of the most challenging task in front

of the Brain Computer Interface (BCI) technolo-

gies. These technologies are used according to the

non-invasive mode (Hajimani et al., 2013), which

is based on capturing directly the Electroencephalo-

gram (EEG) signals from the scalp using the Ag/cl

electrode and without any surgery operation. EEG-

based control system can be conducted by different

SMRs Rhythm such as: Event Related Desynchro-

nization/Synchronization (ERD/ERS) (Duan et al.,

2014), Event Related Potential (ERP) (Cai et al.,

2013), P300 speller (Koo et al., 2014), etc. By using

these techniques, many applications can be developed

to control home equipments, bed nursing, multimedia

devices, games, driving assistance cars, etc.

A typical EEG chain is used in online approach

as depicted in Figure 1. According to the 10-20 sys-

tem standard, the eight electrodes of the OpenBCI ac-

quisition system are ﬁxed on the scalp on these po-

sition: (C1, C2, C3, C4, FC2, CPZ, CZ, FCZ, CPZ,

OZ, Ground electrodes are placed on the ear). EEG

signals are registered using an OpenBCI 32-bit ac-

quisition board based on the ADS1299 Analog front

end device. EEG digital signals are then sent to the

host through RFduino module allowing users to in-

teract freely without any discomfort. The acquired

EEG signal are processed to remove all unwanted sig-

nals. These undesired frequencies are removed based

on adaptive ﬁlters due to the intrinsic variability of

EEG signals in each subject (Belwaﬁ et al., 2014).

Once the EEG signals is well ﬁltered, we proceed to

the extraction of the main characteristic of each trials

by applying the Common Spatial Pattern algorithm;

which is the most effective spatial ﬁlter algorithm to

extract ERD/ERS effects (Kais et al., 2014). The ob-

tained feature is then classiﬁed using the Linear Dis-

Belwaﬁ, K., Djemal, R., Ghaffari, F., Romain, O., Ouni, B. and Gannouni, S.

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery.

DOI: 10.5220/0005846503350339

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 335-339

ISBN: 978-989-758-170-0

335

criminant Analysis (LDA) classiﬁer, which uses lin-

ear function to distinguish between LH and RH tasks

(Lotte et al., 2007).

Even if the off-line validation of the proposed

EEG-based architecture is very interesting using ex-

isting data sets, the on-line validation remains the best

validation one providing a real-time interaction which

represents the feedback aspect in the BCI-chain (Yu

et al., 2015). To proceed for classiﬁcation of LH and

RH movements using the on-line approach, we pro-

posed to use the headset of OpenBCI with eight chan-

nels connected to Matlab environment in which we

developed all EEG signal processing including adap-

tive pre-processing.

EEG

Electrodes

Features

extraction

ClassificationFeedback

EEG Filter

Figure 1: Brain computer interface chain.

The remainder of this paper is organized as fol-

lows. In Section 2, the fundamentals of BCI theory

is described. The data recording and processing ex-

plained in section 3. Section 4 presents the results and

realization of the system. The conclusion and future

research plans are discussed in Section 5.

2 EXPERIMENTAL SETUP

The block diagram of the proposed EEG-based sys-

tem is depicted in Figure 2. Eight EEG channels are

used and placed on the scalp according to the 10-

20 system. The spatial distribution of the electrodes

is depicted in Figure 3. As above mentioned, EEG

signals are acquired using an openBCI32 bit board.

The conversion from analog to digital is done by the

ADS1299, followed by an ampliﬁcation stage with a

factor of 24. The sampling frequency is ﬁxed to 250

Hz. Notch ﬁlters are conﬁgured and applied to all

eight channels as a ﬁrst pre-processing action. The

acquisition board will send continuously EEG data

through the RFduino module to the Matlab environ-

ment via a USB interface (see Figure 4). An auto-

matically script will be launched to check the frames

permanently, converts it to the corresponding value

and stores them into a matrix to be subsequently pro-

cessed. Four volunteers participated in this experi-

Figure 2: Experimental setup.

Figure 3: Electrode positioning.

ment whereas two of them are familiarized with these

experiments. The participants are informed by the

recording scenario to avoid the risk of suffering due

to the long time of recording (Onishi and Natsume,

2013), which can exceed half hour. In total, 260 tri-

als are recorded during these experiments (140: for

training and 120 for test). After launching the process

by press start button, the participant has to imagine

the left hand or the right hand movement according to

the picture appearing on the bottom of the interface as

illustrated in Figure 4.

Figure 4: The interface of the proposed system.

Smart-BIODEV 2016 - Special Session on Smart Embedded Biomedical Devices for In Situ Physiological Signal Processing

336

3 METHOD

Signal processing was performed to keep just the fre-

quencies related to left and right hand motor imagery

which are represented by µ-rhythm and β-rhythm

(Lotte and Guan, 2011). Recently, a new approach

is proposed to select automatically the best ﬁlters pa-

rameters that guarantee the removal of all unwanted

signals, and adapts to the intrinsic individual char-

acteristics of EEG signals for each person (Belwaﬁ

et al., 2014). This method is based on the variation of

the Signal-to-Noise Ratio (SNR) on the stop band that

has an explicitly effect on the pass band frequencies.

Increasing the SNR will increase the ﬁlter order lead-

ing to a modiﬁcation of the transition band to be close

to the cut-off frequency where the signal becomes

very well ﬁltered. Furthermore, the applied adaptive

ﬁltering techniques are based on the Finite Impulse

Response (FIR) and Inﬁnite Impulse Rate (IIR).

Once the EEG signals are ﬁltered, their size

should be minimized as much as possible keep-

ing only useful information contained in each trials.

Many techniques are reported in literature including

power spectral density, Short-Time Fourier transform

STFT, CSP and wavelet analysis (Duan et al., 2014),

etc. The CSP algorithm is strongly recommended for

feature extraction of EEG signal in motor imagery.

This algorithm computes the discrimination between

features of two classes according to the distribution

of their topographic patterns (Robinson et al., 2011).

Formally, CSP computes the normalized covariance

matrices by applying the following equation (Equa-

tion 1):

trace(EE

)

(1)

where trace(x) is the sum of diagonal elements of x,

’i’ is the index of class (LH, RH) and E is data of

each trials of dimension given by Number of channel

x Number of samples. Then, the overall composite

spatial covariance matrices is calculated by adding the

covariance matrices of each classes. In the next step,

the composite matrices (C

) is decomposed according

to the following equation(Equation 2):

= U

(2)

where U

is the matrix containing eigenvector and

is the diagonal matrix containing the eigenvalue

sorted in the ascending order. According to the equa-

tion (Equation 3), the whitening transform will be

computed to equalize the variances in the space that

is created by U

P =

−1

(3)

A simple test can be done to check that all above op-

erations are done successfully. The multiplication of

of P, (U

) and P transpose respectively, should give

one. The transformed covariance matrices S

i∈{1,2}

obtained according to the equation (Equation 4):

= PC

= Bλ

(4)

Then, the projection matrix W is obtained according

to the following equation:

W = B

P (5)

The feature vector which optimally discriminate the

two classes are the (N/2) smallest and (N/2) largest

eigenvector of Z (see Equation 7), where N is the

number of feature that should be retained. In our case,

the number of feature is ﬁxed to six.

Z = W E (6)

Finally, the returned feature vectors is calculated

based on the following equation:

= log(

var(Z

)

var(Z

) + var(Z

)

) (7)

To predict the class of each trials, LDA classiﬁer is

applied on the extracted features. The goal of LDA is

to separate the data representing the two classes by an

hyper-planes as depicted in Figure 5. The equation of

the hyper-plane (L) is mentioned on the same ﬁgure,

where Q and b are the hyper-plane coefﬁcients which

will be estimated during the training phase.

L=W

*E+b

LEFT HAND

RIGHT HAND

Figure 5: A Hyper plane separating LH and RH.

Depending on the sign of L, the classiﬁer will as-

sort each feature. If the hyper plane (L) of an action is

strictly negative, then the action should be a RH. Sim-

ilarly the corresponding actions strictly positive then

is LH.

4 RESULTS

Two sessions are recorded during the evaluation of the

system. One recording session is used for training,

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

337

and the other one for testing. As depicted in Figure

6, the accuracy of the two class motor imagery ac-

tions increases when we integrate the adaptive ﬁlter-

ing techniques for all subjects. In fact, the proposed

BCI chain is able to discriminate RH and LH EEG

signals with a high accuracy reaching 92% for subject

4 for example. The mean performance of all subjects

is close to 80% using adaptive ﬁltering techniques.

Figure 6 shows clearly a signiﬁcant improvement in

the system accuracy by just tuning the ﬁlters parame-

ters. The adaptive ﬁlter is done automatically during

the training phase for each subject and the best param-

eters were ﬁxed during the test phase. Furthermore,

the enhancement of classiﬁcation accuracy over tra-

ditional classiﬁers based on one ﬁxed ﬁlter is signiﬁ-

cant and can reach u;p 25%. The ﬁlters parameters are

heterogeneous among subject. For example for sub-

ject one the best ﬁlters is Chebyschev1 with an SNR

of 50 dB, that implies motor imagery frequencies of

this subject are inside µ and β bands. In others cases

for example, for subject two the LH and RH frequen-

cies are outside these theoretical bands. Information

Transfer Rate (ITR) is used to evaluate the proposed

system according to the following equation:

IT R = L[plog

(p) + log

(N) + (1 − p)log

(

1 − p

N −1

)] (8)

Let consider L is the number of decisions in one

minute, and p is the accuracy of the subject in making

decisions among N targets.

Figure 6: Classiﬁcation accuracy for four subjects.

The ITR for all subjects is 8.64 bits/min which

is very interesting result compared to a similar work.

For example, in (Sannelli et al., 2010), the ITR com-

puted for three subjects is 3.56 bits/min. The system

proposed in (McFarland et al., 2003) dedicated for

controlling the mouse using two motor imagery ac-

tions reaches 7.4 bits/min as ITR for eight subjects.

For comparison purpose with the ofﬂine approach,

the same algorithms are used in the off-line context

and a small increase of only 1% of accuracy. Our re-

sults show that adapting ﬁltering is very interesting to

be integrated into the on-line validation of the EEG-

based motor imagery application to reach a high ac-

curacy.

5 CONCLUSION

The proposed EEG chain shows a clear improvement

of the performance of the system leading to good dis-

crimination between LH and RH motor imagery tasks

for online validation. In order to improve the per-

formance of the proposed system, adaptive ﬁlter was

used to remove the maximum of the unwanted sig-

nals, and to tune the pass band which contain µ and β

band. The proposed method shows that the combina-

tion of the adaptive ﬁlter with CSP as features extrac-

tion and LDA as classiﬁer improves the classiﬁcation

accuracy using the online approach. Furthermore, the

ITR of the system is very interesting comparing with

those obtained from equivalent existing systems.

Our future work target is to extend the proposed

procedure to a multiclass paradigm in order to classify

three tasks that will be used with a state machine to

control home devices equipment. In addition, it will

be interesting to implement the proposed procedure in

real time embedded system.

ACKNOWLEDGEMENTS

This project was funded by the National Plan

for Science, Technology and Innovation (MAARI-

FAH), King Abdulaziz City for Science and Tech-

nology, Kingdom of Saudi Arabia, Award Number

(ELE1730).

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