Single Trial Visual Evoked Potential Extraction using Partial Least Squares-based Approach

2168-2194 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JBHI.2014.2367152, IEEE Journal of Biomedical and Health Informatics

JBHI-00301-2014.R2

Abstract— A single trial extraction of a Visual Evoked

Potential (VEP) signal based on the Partial Least Squares (PLS)

regression method has been proposed in this paper. This paper

has focused on the extraction and estimation of the latencies of

P100, P200, P300, N75 and N135 in the artificial

Electroencephalograph (EEG) signal. The real EEG signal

obtained from the hospital was only concentrated on the P100.

The performance of the PLS has been evaluated mainly on the

basis of latency error rate of the peaks for the artificial EEG

signal, and the mean peak detection and standard deviation for

the real EEG signal. The simulation results show that the

proposed PLS algorithm is capable of reconstructing the EEG

signal into its desired shape of the ideal VEP. For P100, the

proposed PLS algorithm is able to provide comparable results to

the Generalized Eigenvalue Decomposition (GEVD) algorithm

which alters (pre-whitens) the EEG input signal using the pre-

stimulation EEG signal. It has also shown better performance for

later peaks (P200 and P300). The PLS outperformed not only in

positive peaks but also in N75. In P100 the PLS was comparable

with the GEVD although N135 was better estimated by GEVD.

The proposed PLS algorithm is comparable to GEVD given that

PLS does not alter the EEG input signal. The PLS algorithm

gives the best estimate to multi-trial ensemble averaging. This

research offers benefits such as avoiding patient’s fatigue during

VEP test measurement in hospital, in BCI applications and in

EEG-fMRI integration.

Index Terms—Visual Evoked Potential, EEG, Single Trial,

Latent Component, Partial Least Squares

I. INTRODUCTION

HE brain’s response to external stimulus such as visual,

auditory and somatosensory has helped the medical

world in analyzing the brain and also other parts of the

human body [1-2]. In particular, the visual stimulation has

been used to assist doctors in determining the normality or

abnormality of a human’s visual pathway [3].

Manuscript received May 04, 2014; revised September 30, 2014 and

October 21, 2014; accepted October 28, 2014. This work was supported by

Universiti Teknologi PETRONAS, MALAYSIA.

Duma Kristina Yanti is with the Centre for Intelligent Signal and Imaging

Research (CISIR), Electrical and Electronic Engineering Department,

Universiti Teknologi Petronas, 31750 Tronoh, Perak, Malaysia (e-mail:

[email protected]).

Mohd Zuki Yusoff is with the Centre for Intelligent Signal and Imaging

Research (CISIR), Electrical and Electronic Engineering Department,


[email protected])

Vijanth Sagayan Asirvadam is with the Centre for Intelligent Signal and

Imaging Research (CISIR), Electrical and Electronic Engineering Department,


[email protected])

VEP has been used to detect amblyopia [4], glaucoma [5]

and diabetes [6].The subjects are required to respond to a

certain pattern of visual stimuli at some period of time while

electrodes are in place on the human’s scalp to record the

brain signal using an EEG system.Response signals such as

the evoked potentials usually occupy the low frequency,

although other response using visual task such as Gamma

Band oscillations occupy high frequency (30-90 Hz) [7-9].

This experiment requires many trials of stimulation which

leads to the Ensemble Averaging (EA) technique.

Averaging the signals from all of the trials normally causes

a loss of the information that exists in each trial. As such a

method such as a single trial estimation scheme is required to

maintain the unique characteristic of the individual trial. The

single trial extraction assists the integration of EEG-fMRI in

providing the spatiotemporal resolution of the dynamics

cognitive function [10-13]. Morever, in the high accuracy and

high-speed VEP-based BCI system, P300-based single trial

extraction is highly desirable to deliver the communication

with the external devices [14-16].

The single trial extraction of the evoked potential has been

proposed in various methodologies. Independent Component

analysis (ICA) based method has been used in the single trial

evoked potential such as in [9, 17] and moreover with the

functional source separation, the single trial evoked potential

was improved [13]. It is the common method in multi-channel

EEG analysis but not in the single-channel EEG estimation.

Wavelets based methods for single trial evoked potential were

presented in [16, 18]. Despite its online application, the

performance still degrades if the variability in inter-subject is

large. A Kalman-Filter-based approach has been proposed in

[19] for single trial dynamical estimation utilizing the

Recursive Bayesian mean square estimation. This method has

ability in dynamical estimation but it could only be used with

large values for the state noise covariance.

Principle Component Analysis (PCA) based method such as

in [20-21], utilizing iterative generalized eigendecomposition,

has been proposed to extract uncorrelated sources. Another

PCA based method known as generalized eigendecomposition

for the single trial latency estimation by pre-whitening the

colored noise input signal has been proposed by the current

author [22-27].

The Generalized Eigenvalue Decomposition (GEVD)

proposed by one of the current authors [22] is based on the

Eigenvalue Decomposition (EVD) method introduced in [28];

it is implemented if the source signal is corrupted with colored

noise. In GEVD, the pre-stimulation noise covariance is used

to pre-whiten the noisy input EEG signal covariance matrix.

The pre-whitening technique in the generalized

Single Trial Visual Evoked Potential Extraction

using Partial Least Squares-based Approach

Duma Kristina Yanti, Mohd Zuki Yusoff, and Vijanth Sagayan Asirvadam, Member, IEEE

T



JBHI-00301-2014.R2

eigendecomposition which basically alters the input EEG

signal is the key feature to reduce the colored noise.

In this paper, we present a technique based on the Partial

Least Squares (PLS) regression that utilizes the covariance

matrices of the noisy signal matrix and the pre-stimulation

noise matrix through an objective function. The signal and

noise subspace are determined from the eigenvalues of the

objective function. The latent components of the noise with its

projection matrix are deflated from the predictor and response

matrices. The PLS technique, used in BCI using kernel PLS to

analyze the EEG signal, provides the feature extraction for the

classification of motor imagery [29] and classification of the

selective serotonin reuptake inhibitor (SSRI) medication

response [30].

In this paper, we propose PLS to be implemented for the

single trial visual evoked potential extraction. The extraction

of the desired signal using this method has been evaluated

objectively by calculating latency average error rate. The

latency measurement has been carried out on for the peak

latency estimation of P100, P200, P300, N75 and N135 in the

artificial EEG signal. The P100 latency has been used

clinically to measure the response (visual pathway) of the

subject over the stimulus, and to detect the abnormality in the

human’s visual pathway. The N75 and N135 were generated

to show the pattern reversal together with the P100. The P300

has been known to be used for the BCI applications. The P200

was generated in order to show a continuation of evoked

potential signal from P100 to P300.

The mean peak detection and standard deviation have been

estimated for P100 only in the real EEG signal. The standard

deviation is used to justify the range estimation of each

algorithm during the latency measurement. The performance

of PLS is compared with the subspace method in [22].

The paper is presented as follows. In section II the objective

function and the PLS algorithm are explained. Section III

describes the simulation model and the procedure of the

benchmark algorithm. Section IV presents the results of the

artificial EEG and the real EEG signals. Finally, section V

presents the conclusion of the paper.

II. METHODOLOGY

A. PLS and Objective Function

The Partial Least Squares (PLS) regression is a multivariate

analysis to regress the predictor or the independent variable X

and the response or dependent variable Y. In other words, the

role of PLS is to correctly explain the covariance between two

blocks of the independent variable X and the dependent

variable Y by a small number of uncorrelated variables known

as “latent components”. Therefore, the goals of PLS are to

predict Y from X and to describe their common structures by

finding a pair of latent components which produce the

maximum covariance.

In order to find the latent components from both variables,

PLS uses the Singular Value Decomposition (SVD) as its tool

to decompose the covariance matrix of XTY such that:

TT WSCYX

YCU,XWT

(1)

(2)

W is the left singular vector,

C is the right singular vector,

S is the diagonal matrix of the singular values,

T is the latent component of X and summarizes the X variables

for every object,

U is the latent component of Y and summarizes the Y variables

for every object, and

T is the matrix transpose.

From Eq. (1), we can write the SVD of its transpose:

TT

T CSWYX

T2

TTTT

WWS

CSWWSCXYYX

CSC

SCSWWC YXXY

2T

TTTTT

(3)

(4)

(5)

The singular values, S, are equal to the square roots of the

singular vectors of XTYY

TX or Y

TXX

TY. The eigenvalues of

XTYY

TX or Y

TXX

TY give the singular vectors W and C

T such

that:

2iiTT

2ii

TT

Sc cYXXY

)SwwXYYX

(6)

(7)

The aim of the PLS regression is to uncover the latent

components.The latent component T depends on W

eigenvectors. Then we can write a function that can maximize

the eigenvector wi as follows:

q1iT

i λ...λPP

2i

TTTi SwXYYXw

(8)

(9)

The objective function thus can be written with a constraint as

[31]:

1ww to subject wXYYXwmax Ti

TTi (10)

The objective function in Eq. (10), however, does not always

accommodate a condition where besides X and Y variables,

there exists another variable Z which usually is considered as

additional noise. When the covariance noise matrix is forced to be orthogonal with the covariance of X and Y, the objective

function is written as [32]:

,..q1i0ZXw

1wwtosubjectwXYYXwmax

Ti

Ti

TTTi

(11)

The solution to this function is attained by extracting the i-th

eigenvalues of XTYY

TX covariance matrix [33].

The noise itself can be viewed as an aide in the estimation

of variable Y, and therefore, for this case, the covariance of the

noise matrix can be taken into account in the objective

function. By putting the noise covariance matrix in the

objective function, the orthogonality no longer exists between

X and Z. The objective function can be written as [32]:

1wwtosubject

|XwZZXwXwYYXw|max iiii

T

TTTTTT

(12)



JBHI-00301-2014.R2

The PLS accommodates a situation where the noise is not

completely orthogonal to the signal subspace (colored noise).

This is in contrast with the scheme where the noise is assumed

to be orthogonal with the signal (white noise). The covariance

matrices in the objective function can be modified according

to the needs. A trade-off parameter λ is put into the objective

function for the purpose of controlling the trade-off between

maximizing the variance of X and forcing a small covariance

with the noise Z. To solve the problem in Eq. (12), the

Lagrange multiplier operator or regularization parameter γ is

introduced [32]:

1wwtosubject1ww

|XwZZXwXwYYXw|wL

Ti

Tii

iTTT

iiTTT

ii

(13)

Differentiating the Lagrangian function over wiT and applying

the sgn function with α as the argument of the absolute value

operator in Eq. (13) will yield:

iii

TTT

Ti

i wXwZZYYXsgnw

wL

(14)

If ii )sgn(ˆ and by setting the equation above to zero,

then we obtain:

iiiTTT wˆXwZZYYX (15)

Basically Eq. (15) is similar to the eigendecomposition of a

matrix; in this case the matrix is XT (YY

T- λZZ

T)Xwi, resulting

in the eigenvectors wi and eigenvalues .ˆi Since the

optimization function has to maximize the covariance matrix

of the signal then the eigenvectors are based on their

corresponding largest eigenvalues.

B. PLS Algorithm

In this work, the objective function of PLS has been

introduced as:

1ww to subject |ZwZwXwXw|max Ti

Tii

TTi (16)

By following Eq. (14), we extracted the eigenvectors of the

objective function of PLS that maximized the covariance of X,

based on the negative eigenvalues. Once the eigenvectors from

the objective function were extracted, then the latent

component was obtained accordingly, by projecting the

eigenvectors to the corresponding matrix.

XwT (17)

We acquired the loading vectors: P which is the projection of

X to the latent component, and C which is the projection of Y

into the latent component. The least squares problem can be

also viewed as the projection matrix, thus the loading vectors

were obtained as the projection matrix to X and Y:

YT)TT(C

XT)TT(P

T1T

T1T

(18)

(19)

Now, the variables X and Y can be re-written as the

decomposition of its latent component and the corresponding

projection matrix as shown below:

T

T

TCY

TPX

(20)

(21)

Whenever there is a need to obtain the latent component, it

should be obtained by deflating the current variables X0 and Y0

with their corresponding new decomposition of latent and

projection matrices:

Tj

Tj

TCYY

TPXX

(22)

(23)

The same procedure is repeated at the next iteration j until

the variable X has no more latent components to be extracted

using the same objective function. One issue in the PLS

algorithm arises when the matrices are rank-deficient. To cope

with this problem, the Moore-Penrose inverse matrix is

incorporated to solve the issue.

III. SIMULATION MODEL

The mathematical model of the problem is written as:

)()()( kzkykx (24)

where x is the noisy signal, y is the clean (desired) signal, z is

the noise signal and k is the time index. In the artificial EEG

signal simulation, the clean signal was first generated and then

a colored noise using the Auto-Regressive (AR) model was

added to the clean signal to obtain the noisy signal [22].

In order to accommodate the estimation problem, the signal

vector was transformed into a Toeplitz matrix; this can be

written as:

ZYX (25)

The signal vector x and the noise signal vector z in k

samples were constructed into an individual matrix,

respectively. This can be realized by time-shifting the

respective samples into an m x n matrix with the size of the

rows m ≥ columns n to satisfy the PLS matrix model:

)x ... x x( K21K x,

)( K21K z .... z zz (26)

K1Km

1nn2

n1n1

xxx

xxx

xx...x

X

K1Km

1nn2

n1n1

zzz

zzz

zz...z

Z

In the real EEG signal case, the signal vector from each trial

was also transformed into the Toeplitz matrix. As mentioned

previously, Z provided an aide in the estimation of Y. The Z

was assumed to be the noise approach, recorded during the

pre-stimulation period. For the artificial EEG signal, a

generated white noise was assumed to be the pre-stimulation

noise. A different signal to noise ratio (SNR) value from 0 dB

down to -11 dB has been applied to the artificial EEG signal.

The signal and noise generation procedure, described in [22]

has been applied in this work. In the real EEG signal, the pre-

stimulation noise and the corrupted VEP signal in [22] have

also been applied in this work.



JBHI-00301-2014.R2

The algorithm procedures of PLS for the artificial EEG

signal simulation in each SNR, and for the real EEG signal are

as follows:

1. Design the pertinent noisy and noise matrices, X and

Z, accordingly with m ≥ n.

2. Obtain the covariance matrix of X and covariance

matrix of Z and hence, Y = X - Z.

3. Apply the covariance matrices into the objective

function and set the trade-off value, λ=2.

4. Apply the eigendecomposition to the objective

function and obtain the eigenvalues and eigenvectors.

5. Re-arrange the eigenvalues by sorting from the

largest to the smallest value and sort the

corresponding eigenvectors accordingly.

6. Choose the negative eigenvalues γ, and their

corresponding eigenvectors w.

7. Apply Eqs. (17) to (22) and finally obtain the

estimate of Y, as shown in Eq. (23).

The negative eigenvalues were chosen on the basis of the

assumption that these eigenvalues belong to the noise

subspace. By so doing, we were trying to reduce the colored

noise from the subspace. The trade-off value λ =2 was chosen

as the optimum value after many trials on different values.

For comparison purposes, the Generalized Eigenvalue

Decomposition (GEVD) in [22] was adopted. The Kuhn-

Tucker optimization was used in order to minimize the portion

of the noise while at the same time maintaining the distortion

of the signal as small as possible. The Lagrange multiplier was

applied in order to control the minimization of the noise and

the distortion of the signal. In order to pre-whiten the colored

noisy signal, a noise covariance matrix during the

pre-stimulation period was utilized. The complete procedures

of GEVD for the artificial EEG signal in each SNR and the

real signal are as follows:

1. Create the correlation Toeplitz matrices, Rx, Rz, and

Ry = Rx – Rz.

2. Perform the generalized eigendecomposition of Rz,

Ry and obtain the eigenvalues D and their

corresponding eigenvectors V.

3. Re-arrange the eigenvalues D by sorting from the

largest to the smallest values and, accordingly, their

respective eigenvectors V.

4. Choose the eigenvalues that are bigger than zero, for

the purpose of creating the signal plus noise subspace.

5. Using an information criterion, the chosen

eigenvalues are selected again by applying the

Akaike Information Criterion. The selection is meant

to filter some of the eigenvalues and hence, their

respective eigenvectors that are considered to still

belong to the noise subspace.

6. Create a new diagonal eigenvalues matrix Ds with the

eigenvalues chosen based on the Akaike Information

Criterion selection and set the rest to zero.

7. Using the Ephraim and Van Trees optimization, the

formula Hopt for noise reduction is: 1

11

VDVH

sopt

8. Extract the desired signal by multiplying the formula

in step 7 with the noisy signal: .xVDVy1

1s1

Here

the pre-whitening step is shown by the multiplication

of xV1

1

.

IV. PERFORMANCE EVALUATION AND RESULT ANALYSIS

The main purpose in the estimation of the clean (desired)

VEP signal in this work was to measure how far the latencies

of the estimated positive (P100, P200 and P300) and negative

(N75 and N135) peaks were from the ideal positions. The

estimations of the positive and negative peaks were used to

show the pattern-reversal [34, 35] as used in the clinical

environment. The pattern-reversal of VEP showed the evoked

potentials especially at N75-P100-N135; whereas, P200 and

P300 were purposely prolonged to see the ability of the

algorithm. To perceive the idea of the purpose, Fig. 1 shows

the ideal (clean) and the noisy (corrupted) VEP signals of the

artificial EEG signal simulation.

The results are the reconstructed waveforms of the PLS and

GEVD algorithms out of the noisy signal. The reconstructed

waveforms have been purposely chosen from lower SNRs in

order to visualize the capability of the algorithm in poor SNR

conditions. Fig. 2 shows the reconstructed waveforms of both

algorithms for the artificial EEG signal obtained at SNR equal

to -11dB.

From the figure, we can see that the reconstructed

waveform of the PLS algorithm show a closer shape to the

ideal waveform as compared to the GEVD algorithm. This is

evaluated by observing the latency or average error rate of the

reconstructed results. In terms of the amplitude, as can be seen

in Fig.2, the PLS result displays that the amplitude of P100 is

highest followed by the P200 peak and lastly, the P300 peak.

In contrast to the PLS, in the GEVD results, the P300 peak

was higher than the P200.

In order to evaluate the performance of the estimation

algorithm, the latency average error rate has been used to

measure how far the average latency of the positive and

negative peaks was from the ideal positions. In the artificial

EEG signal, the average error rates were obtained for 100

iterations. For the artificial EEG signal of the PLS and GEVD

algorithms, Fig. 3 shows the performance in terms of the

latency average error rate in the peak latencies of P100, P200

and P300.

Fig. 1 Ideal VEP signal shows N75, N135, P100, P200 and P300 in the

corresponding ideal position and its corrupted version at SNR = -8 dB.



JBHI-00301-2014.R2

Fig. 2 P100, P200, P300, N75 and N135 of VEP Extraction at SNR = -11 dB.

We also compare the PLS based method with a non-

prewhitening subspace approach (EVD) [28]. The main

difference between the GEVD pre-whitening based approach

in [22] and the non-prewhitening based approach (EVD) is

that, the GEVD used the generalized eigenvalues

decomposition to pre-whiten the input signal whereas the

EVD used eigenvalues decomposition.

The smaller the average error rate the better the

performance of the algorithm is in reconstructing the noisy signal towards the ideal waveform. As can be seen in Fig. 3,

the latency average error rate of P100 in the PLS algorithm

and GEVD algorithm is comparable. In Table 1, at SNR = -10

dB, the PLS estimation for P100 is 3.29, and the GEVD

estimation is 3.93 which reflect comparable results of P100.

Interestingly, the average error rate for P200 and P300 in the

PLS estimation show smaller values than those of the GEVD

in each of the SNRs. At SNR = -10 dB, the latency error rate

for P200 and P300 in the PLS estimation are 2.88, and 2.58,

respectively, reflecting the smaller error rate than the GEVD

estimation which generated 7.82 and 6.97 for the P200 and

P300.

For the N75 artificial EEG signal, Fig. 4 (a) shows that the

PLS estimation produced a smaller error rate than the GEVD

estimation in almost every SNR. On the contrary, in N135, the

GEVD algorithm has a smaller error rate in comparison to the

PLS algorithm. Table 2 displays the summary statistical values

of N75 and N135 highlighting the significant difference

between PLS and the GEVD estimation. At SNR = -10 dB,

PLS estimation is 3.9 whereas GEVD estimation is 5.07. More

distinct results were also shown in the N135 average error rate

between the PLS and the GEVD algorithms, where PLS

estimation is 11.2 whereas GEVD estimation is 9.1.

At N135, GEVD has given a better estimation than the PLS.

In clinical use, however, the presence or detection of N135 is

usually considered whenever the P100 is difficult to observe

[36]. The overall performance of the PLS has shown

competitive results in comparison to the GEVD.

In general for peak latency estimation, the EVD performed

poorly in comparison to both GEVD and the proposed PLS

methods. Thus in the next measurement comparisons (figures

and table) the EVD based method is omitted for ease of

interpretation and inference.

The PLS latency results in pattern reversal peaks N75, P100

and N135 peak give an alternative promising detection for the

visual pathway as well as in the P300 peak for the BCI

applications. The reason is that the PLS did not need to alter

(pre-whiten) the input signal as GEVD did and yet performed

comparably well.

The single trial amplitudes in terms of mean squared error

(MSE) is also calculated for the artificial EEG signals for PLS

and GEVD as shown in Fig. 5, Fig. 6, Table 3 and Table 4. In

general, the PLS MSE results of positive peaks are smaller

than the MSE of GEVD but for the negative peaks in

particular N75, the PLS MSE is larger than the GEVD. The

MSE results show that the amplitudes of P100 – N135 are

better estimated by the PLS and the amplitudes of N75 – P100

are better estimated by the GEVD.

Fig. 3 Latency Average Error Rate of PLS and GEVD at (a) P100, (b) P200

and (c) P300 as a function of SNR.

TABLE 1

SUMMARY OF STATISTICAL VALUES OF POSITIVE PEAKS

SNR Average Error Rate

(dB) P100 P200 P300

PLS GEVD PLS GEVD PLS GEVD

0 3.68 3.35 2.69 3.7 2.55 5.23

-5 3.51 4.04 2.95 5.56 2.49 5.99

-10 3.29 3.93 2.88 7.82 2.58 6.97

Fig. 4 Latency Average Error Rate of PLS and GEVD at (a) N75 and (b)

N135 as a function of SNR.



JBHI-00301-2014.R2

TABLE 2

SUMMARY OF STATISTICAL VALUES OF NEGATIVE PEAKS


(dB) N75 N135

PLS GEVD PLS GEVD

0 2.15 3.23 8.91 5.6

-5 2.57 3.53 9.71 6.34

-10 3.9 5.07 11.2 9.1

Fig. 5 Mean Squared Error of PLS and GEVD at (a) P100, (b) P200 and (c)

P300 as a function of SNR.

TABLE 3

SUMMARY OF STATISTICAL MSE VALUES OF POSITIVE PEAKS


(dB) P100 P200 P300

PLS GEVD PLS GEVD PLS GEVD

0 0.0093 0.0042 0.0194 0.0276 0.0943 0.1233

-5 0.0098 0.01542 0.0212 0.0458 0.0825 0.1061

-10 0.034 0.0641 0.114 0.1371 0.1136 0.2001

Fig. 6 Mean Squared Error of PLS and GEVD at (a) N75 and (b) N135 as a

function of SNR.

TABLE 4

SUMMARY OF STATISTICAL MSE VALUES OF NEGATIVE PEAKS


(dB) N75 N135

PLS GEVD PLS GEVD

0 0.0137 0.0113 0.0345 0.0507

-5 0.1071 0.1663 0.0905 0.171

-10 0.3264 0.393 0.1469 0.2599

The real EEG signals were collected from 47 patients and

the proposed algorithm was applied to extract the desired

signal and then detect the desired peaks. In the experiment, 80

trials were conducted for the extraction of the real EEG

signals for each of 47 subjects in Selayang Hospital, Kuala

Lumpur, Malaysia. The stimulation used in the experiment

was a checkerboard pattern using checker reversal. The

subjects were required to respond to the stimulus with one eye

closed at any given time [22].

The International 10/20 system was used in the recordings

of 80 trials with the active electrode connected between the

middle of occipital (O1, O2), and the reference electrode was

connected to the middle of the forehead. Sampling frequency

for every trial was at 512 Hz and pre-filtered at 0.1-70 Hz.

In the real EEG signal, only the P100 peak was available for

detection due to the limited length of the recorded signal. The

P100 was investigated as an example, even though the data

would also be sufficient to investigate the N75. Similar to the

artificial EEG signal evaluation, the capability of the

algorithms is also shown in the reconstructed waveforms for

the real EEG signal. Fig. 7 to Fig. 10 show the results of the

reconstructed waveforms of some subjects. The P100 peak has

been detected as the highest peak from time/samples within 80

ms to 160 ms.

The evaluation of the P100 detection in the real EEG signal

case has been initiated by taking the average (mean) peak

detection from every trial. This is due to the absence of

reference peak in every trial. The average error rate in the real

EEG signal thus has been replaced with the mean peak

estimation of single trial and the standard deviation of the

peak estimation has been substituted as the range estimation

shown in Table 5. As can be seen in Table 5, the evaluation is

based on the closeness of the mean peak estimation of single

trial to the Ensemble Averaging estimation.

In the mean peak estimation, out of 47 subjects, the PLS

algorithm has closer estimation for the 21 subjects in

comparison to the EA algorithm. However, the GEVD

algorithm has been able to estimate 26 subjects with the mean

peak estimation closer to the EA algorithm. This result is

similar to our artificial EEG signal where in comparison to the

PLS, GEVD has a mean peak estimation closer to the ideal

P100. For the standard deviation results, the PLS algorithm

showed a smaller deviation than the GEVD algorithm.

Fig. 7 P100 Detection on Subject 2: EA =100, GEVD = 98, PLS = 100.



JBHI-00301-2014.R2



Fig. 10 P100 Detection on Subject 29: EA = 92, GEVD = 93, PLS = 94.

In this single trial estimation work, we have summarized

both results obtained from the artificial EEG signal and the real EEG signal. In the artificial EEG signal, the average error

rates (latencies) are comparable to those of the GEVD.

In the proposed PLS algorithm, the pre-stimulation EEG

signal has been utilized. The core of the PLS algorithm is to

optimize the signal covariance rather than the noise covariance.

Interestingly, it did not normalize the colored noise by

modifying the given input signal as in the case of GEVD.

Since the colored noise was stronger than the desired signal,

the latent component in the PLS algorithm still contained the

colored noise. Hence, when it was projected to the noisy

signal for producing the loading vector, the estimated signal

(desired signal) was still affected by the colored noise. This

scenario was different with the GEVD algorithm; it

normalized the colored noise by pre-whitening the given input

signal. As the colored noise had become white noise, the

GEVD algorithm performed better to reduce the white noise.

This pre-whitening step explains the peak estimation results of

GEVD.

In the PLS the input signal was not modified; the estimation

range was limited to the area in the subspace where the signal

mostly exists along with some portion of the colored noise.

But in the GEVD algorithm, since the input signal was

normalized to whiten the colored noise, the area of the signal

became wider than it should have been, producing a wider

range of estimation which lead to larger standard deviation

reading.

TABLE 5

MEAN AND STANDARD DEVIATION IN REAL PATIENT DATA

Subject EA Mean

GEVD

Mean

PLS

Std

GEVD

Std

PLS

1 99 98.4125 101.3375 10.6837 10.0833

2 100 100.4125 99.925 7.2503 6.6253

3 119 120.325 120.5875 7.4242 5.0539

4 128 127.6375 130.125 8.0035 7.4873

5 99 97.025 101 9.2817 6.1053

6 107 104.8696 105.2174 9.5065 5.9924

7 109 110.8625 110.9125 7.083 5.1239

8 107 108.1375 107.7625 5.4538 6.6657

9 130 132.7875 130.35 23.8074 15.0915

10 119 119.2 120.3125 8.2241 7.3325

11 114 110.7625 115.9625 11.5858 6.5659

12 102 103.5125 101.725 4.5028 5.1647

13 123 122.9375 122.9875 8.7755 9.5335

14 102 104 99.962 7.2973 5.3347

15 102 107.7125 109.4875 9.8529 11.887

16 107 107.0785 106.55 4.6749 5.7661

17 107 106.9625 107.475 9.919 8.7077

18 110 112.7 113.8625 11.4132 9.9508

19 130 130.3125 130.25 5.5398 5.5599

20 109 111.05 112.1875 10.6723 8.1349

21 102 101.3875 113.225 13.635 16.8211

22 130 128.7375 129.7375 7.0405 7.7213

23 104 104.35 104.375 18.0271 6.3253

24 103 103.875 103.4375 5.9856 5.8005

25 116 116.4375 122.6125 17.574 9.0215

26 98 99.0875 98.3375 10.8768 14.4611

27 102 103.2 103.25 14.0435 7.9254

28 101 102.575 103.2 10.0969 9.9038

29 92 94.45 91.6 12.2446 10.1988

30 99 102.3625 96.6375 16.0133 12.2844

31 130 132.5 128.6375 5.8544 11.5534

32 105 104.775 107.2875 14.2276 6.118

33 114 114.1125 116.0875 9.8297 10.9672

34 130 133.3375 131.7375 16.9733 6.9655

35 103 101.2125 104.55 8.0171 7.7941

36 102 100.6625 103.2875 8.3705 7.4719

37 112 113.9 114 10.8524 10.7506

38 106 106 106.4375 12.8047 5.7071

39 114 113.7 112.4625 8.6522 10.2822

40 96 97.0375 97.05 1.3982 6.784

41 104 104.0875 103.95 1.3096 13.379

42 103 103.4 103.95 1.8708 13.379

43 103 105 105.125 3.5267 1.2686

44 94 95.75 95.5 2.9765 3.5707

45 98 97.125 102.25 3.9031 2.9047

46 105 105.125 104.9375 7.3612 3.3998

47 127 130.25 125.6875 14.0435 7.8559



JBHI-00301-2014.R2

V. CONCLUSION

This paper has evaluated the Partial Least Squares

regression-based method for single trial visual evoked

potential extraction. The method utilizes the latent

components of the projection matrix to the independent and

dependent variables. In this paper, the artificial EEG signal

and real EEG signal have been applied to test the capability of

the algorithm. The PLS algorithm was compared to the

benchmark: Generalized Eigenvalue decomposition (GEVD)

algorithm. The results from the artificial EEG signal show that

the PLS-based P100 estimations are comparable to GEVD

ones in term of the latencies. For the real EEG signal results,

21 out of 47 subjects have mean peak estimation close to the

Ensemble Averaging and is almost comparable to that of

GEVD. Moreover, for the standard deviation, the PLS

algorithm has smaller values than the GEVD results.

Further, the P200 and P300 results of the artificial EEG

signals show that the average peak latency of the PLS has a

lower error rate in comparison to that of the GEVD. Overall,

the performance of the proposed PLS technique is comparable

to GEVD as shown in the pattern reversal latency and

amplitude estimation of the artificial EEG signal, as well as

the P100 latency of the real EEG signal without altering the

EEG input signal.

REFERENCES

[1] P. Walsh, N. Kane, S. Butler, ”The clinical role of evoked potentials”, J

Neurol Neurosurg Psychiatry, vol.76, no. 2, pp.16-22, 2005.

[2] Paran, Daphna, Joab Chapman, Amos D. Korczyn, Ori Elkayam, Olga

Hilkevich, G. B. Groozman, David Levartovsky et al.,” Evoked potential

studies in the antiphospholipid syndrome: differential diagnosis from

multiple sclerosis”, Annals of the rheumatic diseases vol. 65, no. 4, pp.

525-528, 2006.

[3] Weinstein G. W,” Clinical aspects of the visually evoked

potential” Trans Am Ophthalmol Soc.1977, vol.75, pp.627–673, 1977.

[4] Halfeld Furtado De Mendoca, R., Abbruzzese, S., Bagolini, B.,

Norfroni, I. and Ferreira, E. (2012), “Importance of visual evoked

potential in amblyopic children”, Acta Ophthalmologica, vol.90, s429,

September 2012.

[5] Jennifer S. Weizer, David C. Musch, Leslie M. Niziol, Naheed W.

Khan,” Multifocal visual evoked potential for early glaucoma Detection”,

Ophthalmic surgery, laser and imaging retina,vol. 43, no. 4, pp.335-340,

July/August 2012.

[6] Raman, P. G., A. Sodani, and A. George,” A study of visual evoked

potential changes in diabetes mellitus”, Int J Diab Dev Count 17, pp.

69-73, 1997.

[7] Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes,

G.R., ”Stimuli of varying spatial scale induce gamma activity with

distinct temporal characteristics in human visual cortex”, Neuroimage 35,

518–530, 2007.

[8] Muthukumaraswamy, S. D., Singh, K. D., Swettenham, J. B., Jones, D.

K., “Visual gamma oscillations and evoked responses: variability,

repeatability and structural MRI correlates”, Neuroimage 49, 3349–3357,

2010.

[9] Porcaro C., Ostwald D., Hadjipapas A., Barnes G. R., Bagshaw A.

P,”The relationship between the visual evoked potential and the gamma

band investigated by blind and semi-blind methods”, Neuroimage 56,

1056-1071, 2011

[10] Debener, S., Ullsperger, M., Siegel, M., Engel, A. K.,” Single-trial EEG-

fMRI reveals the dynamics of cognitive function”, Trends Cogn. Sci. 10,

558–563, 2006.

[11] Bagshaw A. P, Warbrick T.,” Single trial variability of EEG and fMRI

responses to visual stimuli”, Neuroimage 38(2):280-92, 2007

[12] Ostwald D. Porcaro C, Bagshaw A. P, ”An information theoretic

approach to EEG-fMRI integration of visually evoked responses”,

NeuroImage 49 498-516, 2010.

[13] Porcaro C, Ostwald D, Bagshaw A. P, “Functional source separation

improves the quality of single trial visual evoked potentials recorded

during concurrent EEG-fMRI”, NeuroImage 50, 112-23, 2010.

[14] Khan OI, Farooq F, Akram F, Choi MT, Han SM, Kim TS, “Robust

extraction of P300 using constrained ICA for BCI applications”, Med

Biol Eng Comput, 50(3):231-41,2012.

[15] Liparas D, Dimitriadis S, Laskaris N, Tzelepi A, Charalambous K,

Angelis L., “Exploiting the temporal patterning of transient VEP signals:

A statistical single-trial methodology with implications to brain-

computer interfaces (BCIs)”, J Neurosci Methods, S0165-

0270(14)00148-4, 2014.

[16] Tu Y, Hung Y. S, Hu L, Huang G, Hu Y, Zhang Z., “ An automated and

fast approach to detect single-trial visual evoked potentials with

application to brain-computer interface”, Clin Neurophysiol. S1388-

2457(14)00182-5, 2014 .

[17] Khan, O. I, Sang-Hyuk Kim, Rasheed, T., Khan, A, Tae-Seong Kim,

"Extraction of P300 using constrained independent component analysis,"

Engineering in Medicine and Biology Society (EMBC), Annual

International Conference of the IEEE, vol., no., pp.4031,4034, 3-6 Sept.

2009.

[18] Yiheng Tu, Gan Huang, Yeung Sam Hung, Li Hu, Yong Hu, Zhiguo

Zhang, "Single-trial detection of visual evoked potentials by common

spatial patterns and wavelet filtering for brain-computer interface,"

Engineering in Medicine and Biology Society (EMBC), 35th Annual

International Conference of the IEEE , vol., no., pp.2882,2885, 3-7 July

2013.

[19] Georgiadis, Stefanos D.; Ranta-aho, Perttu O.; Tarvainen, M. P.;

Karjalainen, Pasi A., “Single-trial dynamical estimation of event-related

potentials: a Kalman filter-based approach,” IEEE Transactions on

Biomedical Engineering, vol.52, no.8, pp.1397-1406, Aug. 2005.

[20] Hajipour, S.; Shamsollahi, M. B.; Abootalebi, V.,” Visual acuity

classification using single trial visual evoked potentials”, Conf. Proc.

IEEE Eng. Med. Biol. Soc, 2009, pp. 982-985, 2-6 September, 2009.

[21] Hajipour, S.; Shamsollahi, M. B.; Mamaghanian, H.; Abootalebi, V.,

“Extracting single trial visual evoked potentials using iterative

generalized eigen value decomposition,” IEEE International Symposium

on Signal Processing and Information Technology, 2008. ISSPIT 2008,

pp.233,237, 16-19 Dec. 2008.

[22] Kamel, Nidal, Mohd Zuki Yusoff, Ahmad Fadzil Mohamad Hani,”

Single-trial subspace-based approach for VEP extraction,” IEEE

Transaction on Biomedical Engineering, vol.58, no.5, pp: 1383-1393,

2011.

[23] Yusoff, M. Z.; Kamel, N.; Hani, A. F. M., “Estimation of visual evoked

potentials using a signal subspace approach,” International Conference

on Intelligent and Advanced Systems, 2007, ICIAS 2007, pp.1157-1162,

25-28 Nov, 2007.

[24] Yusoff, Mohd Zuki, and Nidal Kamel,” A time-domain subspace

technique for estimating visual evoked potential latencies”, International

Journal of Computer Science and Network Security vol. 10 no. 1,

pp.144-153, 2010.

[25] Yusoff, Mohd Zuki, and Nidal Kamel, ” Estimation of visual evoked

potentials for measurement of optical pathway conduction”, Proc. 17th

European Signal Processing Conference, 2009 (EUSIPCO 2009), 2009.

[26] Yusoff, M. Z.; Kamel, N.; Hani, A. F. M., “Single-trial Extraction of

visual evoked potentials from the brain”, 16th European Signal

Processing Conference (EUSIPCO-2008), 2008.

[27] Kamel, N.; Yusoff, M. Z., “A generalized subspace approach for

estimating visual evoked potentials,” Engineering in Medicine and

Biology Society, 2008. EMBS 2008. 30th Annual International

Conference of the IEEE , pp.5208,5211, 20-25 Aug. 2008.

[28] Ephraim, Y.; Van Trees, H. L., “A signal subspace approach for speech

enhancement,” IEEE Transactions on Speech and Audio Processing,

vol.3, no.4, pp.251-266, Jul 1995.

[29] Xuecai Bao; Zhendong Mu; Jianfeng Hu, “Nonlinear analysis for motor

imagery EEG based kernel partial least squares,” Industrial Electronics

and Applications, 2009. ICIEA 2009. 4th IEEE Conference on , vol., no.,

pp.2106,2109, 25-27 May 2009.

[30] Khodayari-Rostamabad, A.; Reilly, J. P.; Hasey, G.; de Bruin, H.;

MacCrimmon, D., “Using pre-treatment EEG data to predict response to

SSRI treatment for MDD,” Engineering in Medicine and Biology

Society (EMBC), 2010 Annual International Conference of the IEEE ,

vol., no., pp.6103,6106, Aug. 31 2010-Sept. 4 2010.

[31] ter Braak, C. J. F. and de Jong, S, “ The objective function of partial

least squares regression”, Journal Of Chemometrics, vol.12, no. 1, pp.

41-54, 1998.



JBHI-00301-2014.R2

[32] Salari Sharif, S. “Regularized latent variable methods in the presence of

structured noise and their application in the analysis of

electroEncephalogram Data”, Ph.D. dissertation, Chemical Engineering,

McMaster University, Hamilton, Ontario, Canada, 2012.

[33] C. RadhaKrishna Rao,”Use and interpretation of principal component

analysis in applied research”, Sankhya: The Indian Journal of Statistics,

vol. 26, pp. 329–358, Series A 1964.

[34] Odom, J. Vernon, et al. "ISCEV standard for clinical visual evoked

potentials (2009 update)." Documenta Ophthalmologica vol. 120 no.1,

pp.111-119, 2010.

[35] John R. Heckenlively, Geoffrey Bernard Arden, “Principles and practice

of clinical electrophysiology of vision”, MIT press, pp. 300-305, 2006

[36] Understanding DIOPSYS NOVA-TR Waveforms, Dyopsis Inc, 2012.

Duma Kristina Yanti received the B.Sc.

degree in electrical engineering from

Universitas Indonesia, Indonesia. She

later received the M.Sc. degree in

electrical and electronic engineering from

Universiti Teknologi PETRONAS in

2010 and currently doing her Ph.D.

degree in electrical and electronic

engineering from Universiti Teknologi PETRONAS, Tronoh,

Perak, Malaysia.

She is a member of Center of Intelligent Signal and Imaging

Research (CISIR) in Universiti Teknologi Petronas. Her

research interest is in signal processing specifically in

biomedical technology.

Mohd Zuki Yusoff received the B.Sc.

degree in electrical engineering from

Syracuse University, Syracuse, NY, in

1988, the M.Sc. degree in

communications, networks and software

from Surrey University, England, in

2001, and the Ph.D. degree in electrical

and electronic engineering from

Universiti Teknologi PETRONAS,

Tronoh, Perak, Malaysia, in 2010.

He is currently a Senior Lecturer at Universiti Teknologi

PETRONAS. His research interests include signal processing,

telecommunication, and embedded systems. Dr. Yusoff is a

member of the Tau Beta Pi, the National Engineering

Honorary Society, and the Eta Kappa Nu, the Electrical and

Computer Engineering Honorary Society.

Vijanth S. Asirvadam received his

B.Sc degree in Statistics from

University of Putra, Malaysia in April

1997. He later received his Masters

degree in Engineering Computation

with a Distinction from Queen’s

University Belfast and joined the

Intelligent Systems and Control

Research Group where he completed his Doctorate (Ph.D.)

researching into Online and Constructive Neural Learning

methods in November 1999. He took previous employments

as a System Engineer (1999) and later as a Lecturer at the

Multimedia University Malaysia (2003 - 2005). He was also a

Senior Lecturer at Faculty of Engineering and Computer

Technology, AIMST University (2005 - 2006). Since

November 2006, he served as a Senior Lecturer (2006 - 2011)

and later as Associate Professor (2011 - present) at the

department of Electrical and Electronics Engineering,

Universiti Teknologi PETRONAS (UTP).

His research interest in theory includes linear and nonlinear

system identification and model validation. On application

side (mainly on Biomedical Engineering) his main research

interest are on computing techniques in signal, image and

video processing. Dr. Vijanth is member of Institute of

Electrical and Electronics Engineering (IEEE) and also a

member of Institute of Engineering Technology (IET) . He has

over 100 publications in proceedings and journals in the field

of computational intelligence in biomedical technology,

control, signal and image processing.

Dr. Vijanth currently heads the Health Informatics

Modeling (HiM) group for Center of Intelligent Signal and

Imaging Research (CISIR) at UTP. He is a member of IEEE

for signal processing and control systems chapters for

Malaysia section and currently serving as a secretary for IEEE

signal processing (Malaysian Chapter). Dr. Vijanth is also

member of Institute of Engineering Technology (MIET).

Documents

Single Trial Visual Evoked Potential Extraction using Partial Least Squares-based Approach