EEG-based Biometric Identification Using Local ... - IEEE Xplore

EEG-based Biometric Identification Using Local Probability Centers Chengsheng Mao, Bin Hu*, Manman Wang and Philip Moore School of Information Science and Engineering, Lanzhou University, Lanzhou, China *Email: [email protected]

Abstract-In

this paper we propose a biometric solution for

individual identification based on electroencephalography with

to the pattern of behavior of a person, usually including voice [10], gait [11], and electrocardiogram signals [12].

classification using local probability centers. In our study, the electroencephalography signals of a subject are recorded from only one active channel Cz with eyes closed and without any external stimulations. The original signals are preprocessed by Haar wavelet transformation; then a number of features are extracted from the preprocessed signals; and then a classifier with local probability centers are employed to assign the signals to the right person according to the features extracted. By this method we have achieved an average identification accuracy of 96.21 % for a dataset of 11 subjects' electroencephalography patterns and the high F-measure values of different persons has shown that this method performed robustly and effectively for various subjects. In addition, we have studied the variation of recognition accuracy with the time length of electroencephalography sessions and found that a longer electroencephalography session is usually more effective for individual identification. These results are in agreement to the previous research and show the evidence that the electroencephalography carries identity information and a longer electroencephalography session usually carries more identity information. With the simple implementation and good performance, we consider our proposed approach to be suitable for development and implementation in a 'unimodal' biometric identification system or may be combined with other biometric methods to form a 'multimodal' biometric identification system.

Keywords-individual identification, biometrics, EEG, local probability center. I.

INTRODUCTION

Traditional methods to enable the identification of individ­ uals in a broad range of domains and systems have generally utilized passwords, access codes, and card-based systems; such approaches have however been shown to incur significant vul­ nerabilities. For example, these methods may be unworkable if the authorized person loses his/her identity card or forgets the password. To address these security vulnerabilities, biometrics which refers to metrics related to human characteristics and traits, has gained traction driven by the need to implement security in respect of access to secure locations and for data sources in a diverse range of domains and systems [1], [2]. Since biometric modalities are unique to individuals, they are more reliable in verifying identity than traditional token and knowledge-based methods. Biometric modalities are often categorized as physiological and behavioral [3]. Physiological modalities are related to the shape of the body. Common physiological modalities include fingerprint [4], face features [5], palmprint [6], [7], hand geometry [7], iris [8], [9]. Behavioral modalities are related

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Biometric modalities are generally domain specific, there being no optimal modality for all implementations and domain­ s. There are usually recognized issues in using the conventional biometric modalities including: (1) the potential for errors in identification if the biometric features are mimicked or dupli­ cated by imposters [13], and (2) the potential for identification to be ineffective due to physical disability issues. To address the potential issues in respect of conventional biometrics, brain electrical activity has been investigated and is in various stages of development and assessment [14], [15]. Electroencephalography (EEG) as the recording of electrical activity in the brain along the scalp, measures voltage fluctua­ tions resulting from ionic current flows within the neurons of the brain [16]. Due to the uniqueness of each brain configu­ rations, it is expected that people have certain distinct brain patterns that are specific for each individual [17]. A positive benefit of EEG as biometrics is that brain activity is present in all living individuals and it is more fraud resistant compared to conventional biometrics. In addition, EEG is a non-invasive and medically safe approach to the unconscious recording of electrical activity on the scalp. There are currently two basic modes of a biometric system: biometric verification (aka authentication) and identification [3]. Verification attempts to verify whether the individual is the person he claims to be while identification attempts to establish the identity of an unknown individual. Identification is closed­ set if the individual is known to exist in a database, Close-set biometric identification can be considered as the classification of the specified person by the biometric features. However in open-set identification the individual is not guaranteed to exist in the database and the system must determine if the individual is listed in the database [18]. In this paper, we focus on the closed-set biometric i­ dentification and use a classification method based on Local Probability Centers (LPC) to identify individuals. In classifi­ cation problems where class distributions are non-separable or overlapping, classification usually produces incorrect results. To address this issue Li et al [19] have proposed a "nearest neighbor algorithm of local probability centers" which imple­ ments classification based on the LPC for each class. The LPC approach reduces the known instances falling on the wrong side of a decision boundary, which make LPC more robust to noise samples. The reported results support their conclusion that the LPC approach substantially improves the classification

performance of the nearest neighbor algorithm. Thus, it may be effective to apply the LPC approach to EEG-based biometric identification for its robustness to noise. In our method, The original EEG signals are detected from the single active electrode point Cz located on the scalp of the brain when the subjects close their eyes and relax themselves. Next, these original EEG signals are de-noised and then filtered to obtain signals in the alpha, beta, theta band rhythm. Then, we extract a number of features from these rhythms using autoregressive (AR) models and other statistical methods. The LPC classifiers are implemented to classify the subjects (i.e. identify the individuals) using the features. By this method, we show an average identification accuracy of 96.21% for a dataset of 11 subjects' EEG patterns, and the high F-measure values show that the effectiveness can be robust to different subjects. These results validate our proposed approach as being suitable for development and implementation in a biometric identification system. The main contribution from this research can be summarized as follows: •

•

•

•

The high identification accuracy of our primary exper­ iment confirms the evidence that EEG should contain the identity information and individual identification based on EEG is feasible in practice. The comparison between the identification results of LPC and the previously used classification methods for biometrics validates the effectiveness of LPC for EEG-based biometric identification. The usage of single-channel EEG signals makes the data collection and analysis procedure convenient, which may validate the pervasive EEG-based biomet­ ric identification. We have studied the variation of identification accu­ racy with the time length of EEG sessions and found that a longer EEG session is usually more effective for individual identification; that validates the instinct conclusion that a longer EEG session may carry more identity information.

The remainder of this paper is structured as follows: in Section 11 we present an overview of related research address­ ing EEG-based biometric identification. The methodology is discussed in Section Ill. We consider the results of the testing and evaluation in Section IV. The paper closes with concluding observations and consideration of future directions for research in Section V. 11.

RELATED RESEARCH

There is a large body of research in the literature address­ ing investigations into brain electrical activity for biometrics. Pioneering research into the use of EEG in biometrics has been conducted by Vogel [20] and Stassen [21]. And then a number of researchers involved in this study and achieved a series of achievements. Documented research conducted by Poulos et al. in [22]­ [24] consider identification of individuals based on parametric processing of EEG signals using approaches including neural networks (NN) and computational geometry (CG). Poulos et al. have attempted to differentiate subjects individually from

a pool of different individuals by processing parametrical and non-parametrical features of EEG. The correct classification scores 80%C100% was reported based on the experiments involving four subjects and 255 EEG patterns [22]. In [25], the EEG signals of two forehead electrodes (FPl and FP2) were acquired from 51 subjects with eyes closed, and the discriminant analysis could achieve an error rate 3.4%. The subsequent research on the EEG-based biometrics found that using stimulated EEG and more channels of EEG could achieve better results. Marcel et al. [26] proposed the use of a statistical framework of EEG based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, and had demonstrated that there were some mental tasks that were more appropriate for biometrics with other mental tasks being unsuitable. Palaniappan et al. [15] had investigated the biometrics from brain electrical activity from a machine learning perspective; they used Visual Evoked Potential (VEP) signals from 61 activity channels and backpropagation neural network to identify individuals and the maximum classification accuracy was 98 ± 1.26%. Brigham et al. [27] proposed an approach for subject identification from EEG and VEP signals during imagined speech using 64 electrodes, and The subjects were identifiable using linear SVM classifiers with 99.76% accuracy for 6 subjects and 98.96% accuracy for 120 subjects. XU et al. in [28] have analyzed 64-electrode EEG samples for two databases and achieved the respective identification rate 100% and 95.1% with neural network for classification. A detailed review of biometrics from brain electrical activity can be available in [29]. Previous research can achieve somewhat effective results for EEG-based biometric identification. However, the multi­ channel or stimulated EEG collection may increase imprac­ ticability of the biometric system; the single-channel EEG collection in our method is more convenient and unobtru­ sive for users. Moreover, we consider that a longer EEG session should carry more identity information, and enhance the recognition accuracy of biometrics using single channel EEG signals by prolonging the sampling time, one can make a compromise between the sampling time and recognition accuracy. In addition, as for the classification method, we introduce the LPC method which was proposed to reduce the negative influence of noise samples, to our EEG-based bio­ metric identification; as compared to the previous classification methods, LPC shows its advantages for EEG-based biometric identification according to our experimental results. Ill.

MET HODOLOGY

The purpose of this research is to develop an EEG-based solution for individual identification. In this section we set out the research methodology with the data collection, signal preprocessing, feature extraction, normalization, classification, and voting used in our proposed approach to biometric identi­ fication using single-channel EEG signals. The procedures of our study can be modeled in Fig. 1. A. Experimental Setting and Data Collection The population of subjects used in the testing and evalua­ tion consisted of a group of eleven healthy participants aged from 20 to 24. The participants were from different families.

Known

Original

Feature database

EEG

Feature vectors Query

Query

Individual identities

�----+

Voting

�f----!

Classification

Fig. I. The procedures of our study. The original EEG signals were preprocessed before feature extraction from the preprocessed EEG epochs. The feature vectors of EEG epoch with known identity were save into a feature database; the query feature vectors were identified by a classification model created based the feature database. The normalization process was conducted before classification. The identity of an EEG session was achieved by vote of its epochs.

NASi ION

Q--:- -Q �,� �" I

�ec0@�

I

I

\

INION

Fig. 2. Electrode locations for the 10-20 EEG recording system. In our experiments, the EEG signals are detected from Cz point with reference to Al and A2 points.

Fig. 3. The portable and wearable EEG collection device Nexus-4 used in our experiments.

B. Signal Preprocessing In the experiments the subjects were allowed to relax and achieve a calm state prior to conducting the experiment. Subjects were asked to sit in a small and quiet room with their eyes closed and were asked to remain still during the experiment. The raw EEG signals for each subject were then captured and recorded. The common electrode placement system is the 10-20 international positioning system (Standard Electrode Position­ ing Nomenclature, American Encephalographic Association) [30], which contains 19 active and two reference electrodes as shown in Fig. 2. In our method, in order to simplify the system of data collection, the original EEG signals are detected from only one electrode located on the scalp at point Cz with two reference electrodes fixed at earlobes (AI and A2) as shown in Fig. 2. The EEG signals for each subject was measured using a portable and wearable EEG collection device Nexus-4 (Fig. 3) at the sampling rate of 256Hz twice per day in testing conducted over three consecutive days. The first test was conducted in the morning with the second test carried out in the afternoon. Each test of the EEG pattern last 2 minutes.

According to the experimental setting, each subject had six 2-minute patterns a day during which the raw EEG signal data was captured. We then divided each 2-minute session into several 4-second epochs with 2 second overlaps. Therefore, for each subject we conducted 354 (6*59) epochs and the following process is based on these epochs. Raw EEG signals are notoriously noisy and difficult to analyze and features extracted directly from the raw data are not sufficiently robust and reliable for further analysis. Therefore, before the data can be used in our research it should be denoised. Noise present in the EEG signals can be denoised using simple filters and wavelet transformation. In our study, A 40Hz low-pass filter was used to remove higher frequency interference. We then applied the 7-order Haar wavelet transformation to detect and remove electro­ oculogram (EOG) by soft-thresholding [31]. The denoised signals were sufficiently noise free for analysis. Previous research [20], [22] has been shown that alpha and beta rhythms contain significant brain activity frequencies, in the sense that individual genetic characteristics may be mostly contained therein. In addition, theta rhythm can be significant when people are in meditation [32], thus we consider that different people may have different theta rhythms. Then, in

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5

��

2) Hjorth Parameters: Among the features extracted from the time domain signal, three main Hjorth parameters were extracted and used in our work. The Hjorth parameters were derived by Hjorth [33], and had been used in many works and proved to be successful in EEG classification [34], [35]. The three main Hjorth parameters (named Activity, Mobility and Complexity) are explained by Equation 3, 4 and 5, where X' is the difference of signal X, which can be explained by Equation 6.

50· 800 700 600 900 300 200 o 100 400 500 1000

�

(a) Raw EEG signal

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5

50'---� � --:C50 � 0-� O -- ,00-C---:c� 200-�30-C0 -'00 � 00c- ---: o ,Lc0' 0 800-:- -9 600C----=� 700-� -

�

s

�

(b) Alpha rhythm

�

50 = -' 00:- --:-:'OC:-!OO ,00::- ---::= 200 --::: OO:: ---=-SO=-=-O -=600-:: ---::= 700--:::80--=0----9 30--=0 -,:: 0L- ---:-:-

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S

��

50200 o 100

(c) Beta rhythm

�

800 700 600 900 400 500 1000

300

Activity(X)

(d) Theta rhythm

Fig. 4. A sample of raw EEG signal in time domain and the alpha, beta, theta rhythm extracted from it.

our research, The alpha (8-13Hz), beta (14-30Hz) and theta (47Hz) rhythms were extracted from the denoised signals using finite impulse response (FIR) band-pass filters. Signals in other frequency bands were not taken into account in our study. A sample of raw EEG signal and the alpha, beta, theta rhythm extracted from it are shown in Fig. 4.

C.

Feature Extraction

Based on the assumption that the EEG epochs for a specific subject taken in different sessions all relate to that subject, our objective is to identify the nature of the relationship between the EEG signals and the subject. Therefore we must extract the relevant features that may reflect the characteristics of a subject from the EEG epochs. Following the extraction of the waveforms, we perform feature extraction on each of these waveform signals. The features extracted serve as unique descriptors of person's brain activity. In our study, we extracted 8 features from each rhythm. Then there are 24 features for each epoch. The features of a known individual are saved into a feature database as a feature vector; and the feature vector of the query EEG epoch are used as an input of a classifier which is created using the data in the feature database. Let the signal X have N points in time domain with sampling frequency f s, the features in our approach are computed as follows. 1) Statistics Features: Two statistics PPmean and Var were computed from the time domain EEG signal. PPmean denotes the average amplitude of the signal. Computed as Equation 1. Var denotes the variance of the signal. Computed as Equation 2, where X is the average of X.

PPmean(X)

Var(X)

=

1 N

= N

1 N

N

L IX(i)1

i=1

L (X(i) - X)2 i=1 _

(1)

=

Mob·t Z·zty(X)

=

� L X2(i) N

i=1

Activity(X') Activity(X)

-,-----';--::-::c-

(3)

(4)

Complexity(X) = y'Mobility2(X') - MobiZity2(X) (5)

X'(i)

=

{i(i) - X(i - I),

i i

=1 = 2,···,N

(6)

3) Frequency Domain Features: To obtain the frequency domain features, the time domain signal X should be trans­ formed to frequency domain. In our study, AR models were employed for time-frequency domain transformation. There are a number of related EEG-based biometric identification related AR model studies including [23], [36] in which the time series are estimated by a linear difference equation in the time domain. The AR model can be represented by p

X(t)

= - L aiX(t - i) + E(t) i=1

(7)

where a current sample point of the signal X(t) can be modeled as a linear function of p previous sample points plus an independent and identically distributed (i.i.d) white noise E(t). p is the order of AR model. In our study, Akaike's information criterion (AIC) rule [37] was used to estimate the order of an AR model. The order of AR model with the minimum AIC was selected. The representation of AIC is

AlC

= In 152 + 2pjN

(8)

where J2 is the variance of white noise E(t) in the Equation 7, p is the AR model order, and N is the length of signal. The function aryule in MATLAB was applied to build an AR model and to compute the coefficients ai and 152. Then the power spectrum can be estimated by Equation 9. [38]

f(f)

(2) where

=

J2

11

+

L�=1 ake-2jkj7r12

f * f s can be considered as the frequency.

(9)

From Equation 9, f(f) can be regarded as a function of f. We can resolve the maximum value of f(f) and the corre­ sponding f which serve as the two features maximum power Pmax as Equation 11 and center frequency fo as Equation

we set ko max{lO, O.OlN} where N is the total number of the training samples.

=

p(xilwj)

10. The sum power Psum can be obtained by computing the integral of f(f) with respect to f in corresponding frequency band as Equation 12.

fo

= fs * argmaxf(f) f

Pmax

Psum

=

= maxf(f) f

jhi9h/fS f(f)df flow/fs

v

-

Then the estimation of the posterior probability of class Wj is computed through Bayesian rule as

(12)

(16) where Ne is the number of classes and p(Wt) is a priori probability of the (-th class Wt. p(Wt) can be estimated by p(Wt) (Nt/N), where Nt and N is the number of samples in class Wt and the total number of samples, respectively.

=

For the query pattern q, its LPC rlj in class Wj is computed according to

(13)

--­

O"A

where fJA and 0"A are the mean and standard deviation, respectively, of feature A. This method of normalization is useful when the actual minimum and maximum of feature A are unknown or when there are outliers that dominate the min-max normalization [39].

(17)

where

Wj.

x�j (q)

is the set of k-nearest neighbors of q in class

Then the query sample is classified to the class with the nearest LPC from q as

E. Classification The features of EEG are rich in hidden information that can be used to identify individuals. Classification is one of the most important ways in data mining to search the regularity in a large volume of data. In our study, the classification aims to find the correct individual identified by the current EEG signal. The normalized features of an EEG epoch are used as a feature vector to feed into a classifier for individual identification. In our study, the LPC classifiers are employed to classify an EEG epoch to a certain person. The LPC classifier classifies the a query sample in terms of the distance between the query sample and local probability center (LPC) as discussed in [19]. In order to compute the LPC of each categorical set, the class probability of each training pattern Xi must to be estimated in a two step process. Initially, the class-conditional density of each pattern is estimated; this is followed by the computation of the posterior probability of the corresponding class using the Bayesian rule. For each training pattern Xi its categorical set consist­ ing of ko-nearest samples from class Wj, which is denoted by (Xi), is used to estimate its class-conditional density p(xilwj) as Equation 14, where cjJ(x s ) in our experiment is the Gaussian kernel defined as Equation 15, where q and T are fixed constants; in our experiments, we set q 1 and T 2. As ko should be a small fraction of the total samples,

X��

=

=

(14)

(11)

'

fJA

i

XSEXk � (X )

cjJ(X s )

(15)

To prevent features with an initially large range from induc­ ing bias by out-weighing features with initially smaller ranges, each feature requires normalization. In our experiments, we use the z-score normalization that linearly transforms each of the numeric feature with mean value 0 and standard deviation 1. A value of feature A is normalized to by

' v =v

0

(10)

D. Normalization

v

1 = IkoT

c

= arg.

min

)=1,.·· ,Ne

d(rlj, q)

(18)

where d(rlj, q) represents the distance between rlj and q. In our experiments, the 'nearness' involved in our work is defined in terms of Euclidean distance. In our experiments, we have also employed other classifiers previously used for biometrics as controls including k-Nearest­ Neighbors (kNN), Support Vector Machine (SVM), Linear discriminant analysis (LDA), Artificial neural network (ANN) and Naive Bayes Classifiers (NBC). •

•

•

kNN: The traditional kNN rule, where the unknown sample is assigned to the most common class among its k nearest neighbors. SVM: SVM has developed rapidly in recent years and has achieved success in many application tasks. In our experiments, we use the libSVM toolbox [40] with Gaussian radial basis function (RBF) kernel and default parameters. NBC: NBC is a Bayesian classifier that assigns a given sample to the class with the highest posterior probabil­ ity [39]. In our experiments, the posterior probability is computed under the class conditional independence assumption and with kernel density estimation.

•

•

F

LDA: LDA tries to find a linear combination of features which characterizes or separates two or more classes of objects or events [41]. In our experiments, we employed the Matlab Statistics Toolbox function classify for classification. ANN: In our experiments, we create a feed-forward back propagation neural network (BPNN) [42] with a single hidden layer to classify the EEG epochs. The output nodes are set at 11 so that the BPNN can classify an EEG epoch into one of the 11 individual categories. The number of hidden layer nodes is set at 10.

Voting

To classify an EEG session we classify all the epochs contained in the EEG session; we then determine the classes of the EEG sessions according to its epochs. In our method, the class of an EEG session is voted by its epochs, i.e., the EEG session is assigned to the class that the majority of its epochs belong.

>() 0.9

�

:::l () () !1l

0.8 o

RESULTS AND DISCUSSION

As described above, EEG patterns for each subject are collected over 3 consecutive days. To provide confidence in the results we use a 3-fold cross validation process to test the performance of each classifier. The EEG data recorded in one day was used as the training set and the remaining two days data are used as test set. We repeat the process 3 times with different training data corresponding with 3 days data. The identification performance is averaged over the 3 iterations.

PC L -I I - .. - kNN

10

15

20

25

30

k

Fig. 5. The variation of identification accuracy with the parameter k for LPC and kNN.

0.95 >-

0.9

�

0.85

8

0.8

:::l

.....

IV.

0.85

!1l

....LPC ...

-. -kNN -0-

0.75

SVM

o NBC

-·-LOA

�BPNN

10

20

30 n

40

50

60

Fig. 6. The variation of identification accuracy with EEG session length for different classifiers.

Consider a I -minute EEG pattern as a typical session. A session contains 29 4-second epochs and a 2-minute EEG pattern consisting of two sessions; therefore there are 4 (2*2) sessions for each subject in one day. In our experiments, we consider each session as a data sample. After each epoch of a session is classified to a certain subject by a classifier the EEG session is assigned to the subject that the majority of its epochs have been assigned to. Thus, the identification accuracy is the number of correctly classified EEG session divided by the total number of EEG sessions.

sample would contain distant samples that are probably not in the same class as the query sample; therefore it is potentially questionable to assign the most common class as the class of the query sample. While for a LPC classifier, the parameter k denotes the number of nearest neighbors used to estimate the LPC for each class; the LPC can be regarded as a weighted average of the k nearest neighbors; therefore the effect of the parameter k of LPC on the identification accuracy is not so significant.

For a LPC classifier, the parameter k presents the number of nearest neighbors in each class and it reflects the locality of this algorithm. For a kNN classifier the parameter k also reflects the locality. To analyze the effects of the locality of LPC and kNN on the individual identification accuracy, the identification accuracies are computed using LPC and kNN with various k values. Fig. 5 plots the variation of identification accuracy with the parameter k for LPC and kNN. In Fig. 5 k denotes the average number of nearest neighbors in each class, i.e., the total number of nearest neighbors is k * Ne for kNN.

For each classifier we have studied variations in the iden­ tification accuracy and EEG session length. Given that the EEG signals are recorded continuously we considered several (assumed to be n) continuous 4-second epochs from the the same subject as an EEG session; thus, a 2-minute EEG pattern can contain l59 / n J EEG sessions. The variable n can denote the EEG session length by t 2 * n + 2(s ) where t is the time length of an EEG session. We plot the variation of identification accuracy with n in Fig. 6 where the value of parameter k of kNN and LPC are 1 and 28 respectively.

From Fig. 5 kNN and LPC can achieve the optimal performance at k 1 and k 28 with the corresponding accuracy 94.70% and 96.21% respectively. We can also see that with the parameter k increasing the identification accuracy of kNN decreases clearly while that of LPC does not change greatly. Thus, the LPC classifier can be more robust than kNN to the locality for individual identification. For a kNN classifier, if the k value is too large, the k nearest neighbors of the query

From Fig. 6 we can see that for all classifiers the identi­ fication accuracies roughly rise with the increase of n which show the evidence that a longer EEG session may carry more identity information with more effective identification. Thus, to achieve a higher identification accuracy, we should sample longer EEG sessions. However, with the EEG session length increasing, the growth in the identification accuracies become increasingly slow and when n is larger than about 29, the

=

=

=

TABLE l.

Subjects A B

THE RECALL VALUE OF EACH SUBJECT FOR ALL THE CLASSIFIERS LPC I

kNN

D

0.9583 0.9167 1

0.9583 0.9167 1

F

0.9583 1

0.9583 1

C E

G

H 1

J

K

SVM

I

I

0.9167 0.9583 0.9583 1 1

LDC

NBC I

I

0.9167 0.8750 0.8333 1

BPNN

Subjects

I

A B

0.8750 0.8333 0.9167 1

0.8750 0.8333

0.9167 0.9583 0.9167 0.9583 0.8750 1

0.9167 0.9167

0.9583

0.7917

0.9583 1 0.9583

0.9167 1 1

0.9583 0.9167 1 1

0.8333 1 1

0.9167 1 1

0.8750 1 1

0.8750

0.8750

0.8333

0.8750

0.7917

0.8333

TABLE H.

C

- measure

=

2

x

Precision

x

Recall

Precision + Recall

.

I

J

K

(19)

Fixing n at 29 (i.e., the EEG session length is 1 minute) we have also analysed the performance of the classifiers for each subject. Table I, 11 and III respectively show the recall, precision and F-measure of each subject for all the classifiers. From Table III we can see that the F-measure of LPC for each subject is higher than 0.9, which denotes that LPC can classify each subject effectively. While other classifiers fail to produce equally effective results for some subjects. V.

CONCLUSION

In this paper we have investigated an EEG-based biometric identification approach. We have presented our posited ap-

1 1

F

TABLE Ill.

F

0.9600 1 1 0.9231

0.9200 1

G

Recall and precision are two widely used metrics for evaluating the performance of a classifier on a certain class; for a detailed discussion see [43]. In a classification task, recall is defined as the number of samples correctly labelled as belonging to this class divided by the total number of samples that actually belong to this class; precision is the number of samples correctly labelled as belonging to this class divided by the total number of samples labelled as belonging to this class. Recall and precision respectively indicate the accuracy and purity of classification for a certain class. F­ measure combines precision and recall as one indicator and can reasonably evaluate the performance of a classifier for each class. F-measure is the harmonic mean of precision and recall denoted as Equation 19; a larger F-measure value denotes a better performance of the classifier on the corresponding class.

kNN

1

0.8571

H

While identification accuracies for all the classifiers show similar trends related to EEG session lengths, LPC performs better than other classifiers for long EEG sessions. From Fig. 6, if n is large, actually n � 29 in our experiment, the iden­ tification accuracies of LPC are higher than other classifiers; while if n is small, SVM performs more effectively. The other classifiers perform moderately for individual identification. However, to achieve a higher identification accuracy, a longer EEG session is required in which case the LPC approach should provide more effective identification.

LPC

D E

increase in the accuracy is not so significant. Accordingly, for a specific application of individual identification, there should be a trade-off between the identification accuracy and EEG sampling time.

THE PRECISION VALUE OF EACH SUBJECT FOR ALL THE CLASSIFIERS SVM

NBC

LDC

BPNN

1

1

1 1 0.8214

0.9231 1

0.9130 0.8333

0.9231 1 1

0.8846 1 1

0.9231 1 0.9565 1

0.9583

0.9565 1 0.7742

1

1

0.9200 0.9583 1

0.9583

1

0.8571 1 1

0.9200 0.8800 0.8846 1

0.7857 0.8571 0.9565 0.9565

0.9600

0.9091 0.8000 0.9600

0.9600 0.9167 0.9600 1

0.8889

0.9524

0.8400

0.9048

0.9091

0.9545 1

THE F-MEASURE VALUE OF EACH SUBJECT FOR ALL THE CLASSIFIERS SVM

NBC

LDC

BPNN

1

1

0.9565 0.9787

0.9565 0.8936

0.9600 0.9565 0.9388

0.9600 0.9200 1

0.8846 0.9600 1

0.8333 0.9231 0.9333

0.8980 0.9200 0.9333

0.9600 0.9333 0.9091 0.8462 0.9231 0.9362

I

0.9388 0.9583 1

0.8837 0.9362 1

0.9583 0.9362 1

0.9091 0.8696

0.9796 0.9167

K

0.9583 0.9333

0.8727 0.9333

0.9796 0.8889

0.8889 0.9796 0.8571

0.9796 1 0.8444

Subjects A B C

D E F

G

H J

LPC

kNN

1

0.9796 0.9787 0.9565

0.9787 0.9565 0.9231 0.9388 1

0.9362 0.9130 1 0.9412 0.8696

proach which is predicated on the LPC algorithm for classifica­ tion. In our approach, The EEG signals collected from a single Cz electrode of an individuals were split into epochs. Then the epochs were processed by preprocessing, feature extraction, normalization and classification; then an EEG session was assigned to the subject that most of its epochs were labeled to. In this research, we have focused on the close-set identifi­ cation. The experimental results on 11 health subjects showed that the identification accuracy roughly rose with the time length of EEG session increasing. With the time length of EEG session set I -minute or longer, the LPC classifier showed its better effectiveness than other classification methods and achieved an identification accuracy not lower than 96.21%. The high identification accuracy might show that EEG signals should carry identity specific information and a longer EEG session would carry more identity information. Accordingly, EEG signals are appropriate for designing biometric identifi­ cation systems. In considering future work we will investigate open-set identification to determine whether the individual to be identified is in the database and the verification to determine whether an individual accords with his/her claimed identity.

ACKNOWLEDGMENT

This work was supported by the National Basic Research Program of China (20l 4CB744600), the International Co­ operation Project of Ministry of Science and Technology (2013DFAI1140) and the National Natural Science Foundation of China (61210010).

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