Human Identification with Electroencephalogram (EEG) Signal Processing

Xu Huang Faculty of Information

Sciences and Engineering University of Canberra,

ACT 2601, Australia Xu.Huang@canberra.edu.au

Salahiddin Altahat Faculty of Information

Sciences and Engineering University of Canberra,

ACT 2601, Australia Salah.Altahat@canberra.edu.au

Dat Tran Faculty of Information

Sciences and Engineering University of Canberra,

ACT 2601, Australia Dat.Tran@canberra.edu.au

Dharmendra Sharma Faculty of Information

Sciences and Engineering University of Canberra,

ACT 2601, Australia Dharmendra.Sharma@canberra.edu.au

Abstract—Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. It has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With neural network (NN) model, our analysis showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334×10-7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications become true in the foreseeable future

Keywords-security system, biometric nature, EEG, neural network, signal processing

I. INTRODUCTION Human identification becomes huge demand in particular

for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. But they are sometimes can be imitated as a lot of cases shown. Recently, it is noted that non-invasive brain-computer interface (BCI) becomes very attractive area as it uses a variety of brain signals as input, for example, electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and near infrared spectroscopy (NIRS). MEG, fMRI, and NIRS are expensive or bulky, and fMRI and NIRS present long time constants in that they do not measure neural activity cannot be deployed as ambulatory BCI systems.

EEG signals are the signatures of neural activities. They are captured by multiple-electrode EEG machines either from

inside the brain, over the cortex under the skull, or certain locations over the scalp, and can be recorded in different formats. The signals are normally presented in the time domain, but many new EEG machines are capable of applying simple signal processing tools. Such as the Fourier transform to perform frequency analysis and equipped with some imaging tools to visualize EEG topographies. There have been many algorithms developed so far for processing EEG signals. The operations include, but are not limited to, time-domain analysis, frequency-domain analysis, spatial-domain analysis, and multiway processing.

Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris palm print, voice print, retina, DNA, or even posture. Since each biometric modality has its own perspectives and constraints, people have been exploring new modalities for usage in different situations. Up to the present, EEG signals have been successfully applied to the research and development of brain-computer interfaces whose main goal is to enhance the communication and control abilities of motor-disabled people [1-5]. Comparing with other biometric features, EEG has several advantages as follows: (1) it is confidential (as it corresponds to a mental task), (2) it is very difficult to mimic (as similar mental tasks are person dependent), (3) it is almost impossible to steal (as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase).

In this paper we are building a concept of brain print and assuming that EEG signal alone is able to create a unique pattern for each subject. In other words we are not going to combine any other human feature with EEG signal to identify people. We are considering working on large number of peoples with two public databases and using simple feature extraction and simple classification methods to provide strong evidence that our novel algorithm with EEG signal processing can provide unique patterns to identify people with other human features.

The paper consists of five sections, in section 2 we shall show some related works in this area and then proposed our novel algorithm in section 3. In section 4 we show our simulation results and the conclusion of this paper will be presented in section 5.

2012 International Symposium on Communications and Information Technologies (ISCIT)

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II. RELATED WORK Up to the present, EEG signals have been successfully

applied to the research and development of brain-computer interfaces whose main goal is to enhance the communication and control abilities of motor-disabled people. For biometrics, it is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. As mentioned that there are some advantages for using EEG rather than other human features. However, till now, little work has been done on EEG-based biometrics.

With a data set of four subjects and 255 EEG trials (subjects were at first with eyes closed) Poulos et al. adopted two classification algorithms and obtained the accuracies of around 80% and 95% respectively [1-2]. Paranjape et al. analysed a data set of 40 subjects and 349 EEG trials (subjects were resting with eyes open and closed) and got a classification accuracy of about 80% [3].

Palaniappan and Mandic carried out a personal identification experiment with 102 subjects based on visual evoked potentials and the accuracies were around 95-98% [4].

Marcel and Mill´an got a highest accuracy rate for personal verification of 93.4% [5].

The above early work has played an important role in studying the feasibility of EEG signals for usage in biometrics. However, when learning a classifier, they all adopted only one kind of brain activity.

Recent research on multitask learning indicates that the performance of a main task can be improved by learning related tasks together [6, 7].

An authentication (or verification) system involves confirming or denying the identity claimed by a person (one-to-one matching).

In contrast, an identification system attempts to establish the identity of a given person out of a closed pool of N people (one-to-N matching).

Authentication and identification share the same processing and feature extraction steps and a large part of the classifier design. However, both modes target distinct applications. In authentication mode, people are supposed to cooperate with the system (the claimant wants to be accepted). The main applications are access control systems (airport checking, monitoring, computer or mobile devices log-in), building gate control, digital multimedia access, transaction authentication (in telephone banking or remote credit card purchases for instance), voice mail, or secure teleworking. Potential applications include video surveillance (public places, restricted areas) and information retrieval (police databases, video or photo album annotation/identification).

Measuring the EEG is a simple non-invasive way to monitor electrical brain activity, but it does not provide detailed information on the activity of single neurons (or small brain areas). Moreover, it is characterized by small signal amplitudes (a few Volts) and noisy measurements (especially if recording outside shield rooms). Besides electrical activity,

neural activity also produces other types of signals, such as magnetic and metabolic, that could be used in a BCI.

Shedeed in [6] used voting scheme for different features extraction methods which are Discrete Fourier Transform and Wavelet Packet Decomposition both with different measures, and used neural network back propagation classifier and it was claimed that it reached an accuracy of 100%, but the number of classes was only three subjects only. Yazdani et al. in [7] works on a partial set of the same dataset we worked for visual evoked potentials. They used different features extraction methods which are autoregressive model (AR) model parameters and the peak of power spectrum density (PSD). Then use LDA to reduce features and the K-nearest neighbour (KNN) classifier. They also claimed that it reached 100% accuracy over 20 subjects when AR model equal to or greater than 14. The proposed method is more complex and the number of subjects is less than what we are considering in this work. Riera et al. in [8] select the best five features set among multifeatures preliminary work. The best five features are AR model, Fourier Transform, Mutual Information, Coherence and Cross Correlation. These features were selected on different channel configuration. The number of the sample was 51 peoples and 36 intruders. They use Fisher's Discriminant Analysis classifier with four different discriminant functions. They reach a performance between 87.5% to 98.1%. Their proposed method is depending on high computation, and the number of subjects considered in this work approximately doubled. Poulus have many contributions in this field all with small number of subjects. The latest one was Poulus et al. [9] where they reach a classification rate around 99.5%. Palaniappan [10] used a total of 61 channels to record Visual Evoked potential (VEP) EEG signals from 20 subjects. He used the spectral power for the gamma band (30 − 50) Hz as a feature. The reached average accuracy was 99.06 with a 10 fold cross validation. Also Palaniappan et al. in their work in [11] to update the used methods in [3] and test the used methods against larger sample, the result drops to less than 95% when reaching 40 peoples.

III. PROPOSED ALGORITHM In order to make human identification system more

effective and efficient, we particularly focus on the simplest algorithm for decreasing the calculations and shorten latency.

In this work we are trying to test the EEG uniqueness over a large number of subjects, and also try to use simpler method for feature extraction to make EEG identification more applicable. So in this work we will:

1. use EEG identification method on a large number of subjects to emphasize EEG uniqueness among peoples. This will enhance the opportunity to use EEG identification on large scale, or even to use it as a universal human identity.

2. use relatively low complexity and low computation cost methods in pre-processing and feature extraction, to enhance considering EEG as an online solution for human identification. In this work we tackled the above concerns by using large public database that contains EEG data for (i) 45 people and (ii) 122 people. Also all the processing are only

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considering rms spatial pattern only to create feature vector which is used for the first time in EEG.

There are many debates about EEG bandwidth and it is noted that significant signals are distributed within lower than 100 Hz, for example Howard et al. [2] where they suggest upper limit to gamma in EEG bandwidth to 60Hz. A typical set of EEG signal during a few seconds for an adult brain activity are as shown in Figure 1 [12]. Therefore, in the pre-processing step, all the EEG signals were filtered to get frequencies between 0 and 60 Hz. All frequency components above 60 Hz were disregarded.

Figure 1: A typical set of EEG signal during a few seconds of normal adult brain activity [12].

This filtration is done over all the EEG signals from all the 64 electrodes as shown in Figure 2. There shows an example of the effect of the filtration on of the EEG signals.

Figure 2: Low pass filter for obtaining lower than 60 Hz EEG signals

For the extraction of the human feature, the whole processing will only take EEG low pass signals and mapping them into the rms value for each special position or each electrode. Each rms value, denoted as x, can be obtained from the well know definition of equation (1):

2

1

1( )n

kk

rms x xn =

= ∑ (1)

and the rms value represents active potential of the signal where power of the signal p(x) is directly proportional to the rms value, as shown below:

2( ) ( )p x rms x∝ (2)

We have designed the EEG sample from each electrode is divided into one second time period length signals including 256 values, and the rms values for all the 256 values are calculated with equation (1) then sending them to feature vector. For this case we obtained feature vectors of length 64 rms values that taken from the related electrodes as shown by Figure 3.

Figure 3: 64 vectors transformed from corresponding 64 electrodes.

A neural network (NN) classifier is designed to classify the obtained data.

The NN classifier is feed forward error back propagation network. Training starts from a random weight set. Also the NN engine uses batch mode for weight update after the end of each epoch. The NN engine uses the continuous tan sigmoid activation function. The NN is designed with 64 nodes in the input layer, which is the same number of electrodes. The number of outputs depends on the number of subjects which is 45 for the first experiment and 122 for the second experiment. The network has 45 neurons hidden layer in the first experiment, and 70 neurons hidden layer in the second experiment. In the second experiment which was operated on 122 subjects, the NN classifier was in another configuration where the input layer has a direct connection with both the hidden layer and the output layer which gives good increase in the classification ratio. We used the MATLAB built in nntraintool tool to run the tests. The rms feature vector input was pre-processed by this tool by normalizing the data between [1, -1].

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As mentioned in above that the dataset was taken from the public data repository for machine learning [1]. This dataset was collected through a study was performed at the Neurodynamics Laboratory of the State University of the New York Health Centre at Brooklyn. This study EEG correlates of genetic predisposition to alcoholism. The dataset contains multiple measurements from 64 electrodes placed on subject's scalps which were sampled at 256 Hz (3.9 ms epoch) for 1 second.

There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2. There were 122 subjects and each subject completed 120 trials where different stimuli were shown. Zhang et al. (1995) describes in detail the data collection process.

IV. EXPERIMENTS, RESULTS AND DISCUSSION The original data contains 77 alcoholic subjects and 45

control subjects. In the first experiment we consider half the samples available for all 45 control subjects.

The samples were selected randomly. The input layer size is 64 inputs which is the number of rms value for each electrode. The NN back propagation with one hidden layer with a number of neurons equal to the number of outputs (45), and the output layer which represent the number of subjects (45 control peoples). The NN engine by default normalizes the data between 1 and -1 for the input and output. The training stopped when the classifier reached below the minimum gradient which is set to 10-6.

Obviously, the results were so promising, and the classifier was able to identify all the 45 peoples correctly, with a mean square error value of 1.98842 ×10-7.

Figure 4 shows the mean square error and Figure 5 shows gradient during the training. The training stopped after 364 epochs.

Figure 4: Mean square errors for 45 subjects during the training processing.

The similar design was used to the second dataset. The target is trying to check if this algorithm has generalization for dealing with other EEG signal. In fact the designed algorithm with its simple, low complexity, high resolution, effective and efficient is very attractive for the real life applications, in particular for the high density population places.

Figure 5: Gradient for the first database taken from the public open resource for 45 people (subjects) during the training processing.

The second database is about 122 people in comparison the first database the size is almost three times as the previous one, which is obvious a good challenge to the designed algorithm.

Also this will verify if the rms spatial pattern can be considered as a brain signature or brain print. In the second experiment the input size remain the same which 64 rms inputs for the EEG electrodes. The hidden layer size was increased to be 70 neurons arbitrarily. And the output size is 122 which is the number of peoples. As in the first experiment the NN engine by default normalizes the data between 1 and -1, and the continuous tan sigmoid activation function was used.

Although we consider bigger number of peoples, the results was also promising.

The classifier was able to identify 113 peoples correctly out of 122, with a mean square error value of 0.00271. The other nine subjects were clarified the case that the classifier was not able to identify: four of nine were highly confused with other subject in the sample, and five were not identified totally. Figure 6 shows the mean square error and Figure 7 shows the gradient during the training.

Figure 6: Mean square errors for the second database, 122 people (subjects) during the training.

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To enhance the efficiency of the classifier in the second experiment, we add a weighted connection between the input layer and the output layer. The efficiency increases after this enhancement, and the classifier was able to identify 116 peoples correctly out of 122, in other words 95.1% successful rate.

Figure 7: Gradient for the second database for 122 people (subjects) during the training.

The mean square error value was 0.00186. The other six subject that the classifier were not able to identify, four of them were highly confused with other subject in the sample, these four are different than the four in the first part of this experiment. The other two were not identified totally. This last experiment shows that by enhancing the classifier the result might enhance and a better classification rate might be achieved through using the rms spatial pattern as a feature vector. Figure 8 shows the mean square error and Figure 9 shows the gradient during the training of this experiment.

Figure 8: mean square error for the second database for 122 people (subjects) during the training with enhanced classifier.

Figure 9: Gradient for the second database for 122 people (subjects) during the training with enhanced classifier.

Obviously the final outcomes are very encouraging as shown by the Figures 8 and 9 which imply that this algorithm could have the potential for the real life applications.

V. CONCLUSION In this paper we have been focusing on one of non-invasive

brain computer interface (BCI) signal, a typical variety of brain signals, electroencephalography (EEG) as input to analysis its characteristics. Those characteristics are used to identify the people as other biometrics to recognize and distinguish people based on their physical or behavioral features. As using EEG signals to identify people has some advantages such as it is confidential, it is very difficult to mimic and it is almost impossible to steal, etc. EEG signal processing has drawn great attentions as this paper does. A novel algorithm is presented in this paper. Our designed classifier is able to identify all the 45 people correctly with a mean square error of 2.0334×10-7 for the first public open database and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186.

We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications become true in the foreseeable future.

REFERENCES [1] M. Poulos, M. Rangoussi, V. Chrissikopoulos, A. Evangelou. Parametric

person identification from the EEG using computational geometry. Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems, 2:1005–1008, 1999.

[2] R. Paranjape, J.Mahovsky, L. Benedicenti, Z. Koles. The electroencephalogram as a biometrics. Proceedings of the Canadian Conference on Electrical and Computer Engineering, 2:1363–1366, 2001.

[3] R. Palaniappan, D. Mandic. Biometrics from brain electrical activity: A machine learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):738–742, April 2007.

[4] S. Marcel, J. R. Mill´an. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.

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[5] Sebastien Marcel a Jose del R. Millan Person authentication using brainwaves (EEG) and Maximum a posteriori model adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence Special Issue on Biometrics 2007

[6] H.A. Shedeed, “A new method for person identification in a biometric security system based on brain eeg signal processing,” Information and Communication Technologies (WICT), 2011 World Congress. Dec 2011. Pp 1205-1210.

[7] A. Yazdani, A. Roodaki, S.H. Rezatofighi, K. Misaghian, and S.K. Setarehdan, “Fisher linear discriminant based person identification using visual evoked potential,” Signal Processing, 2008 ICSP 2008, 9th International Conference. Oct. 2008, pp1677-1680.

[8] A. Riera, A. Soria-Frisch, M. Caparrini, C. Grau, and G. Ruffini, “Unobtrusive biometric system based on electroenphalogram analysis,” EURASIP, J. Adv. Signal Process 2008.

[9] M. Poulos, M. Rangoussi, N. Alexandris, and A. Evangelou, “Prson identification from the EEG using nonlinear signal classification,” Methods Inf. Med 41 (2002), No. 1, 64-75.

[10] R. Palaniappan, “Method of identifying individuals using vep signals and neural network,” IEE Proceedings-Since, Measurement and Technology 151 (2004), No. 1, 16-20.

[11] Ramaswamy Palaniappan and Danilo P. Mandic, “Eeg based biometric framework for automatic identity verification,” J. VLSI Signal Process. 49 (2007) No.2, 243-250.

[12] Saeid Sanei and J. A. Chambers, “EEG Signal Processing” Centre of Digital Signal Processing Cardiff U&niversity, UK John Wiley & Sons , Ltd. 2007.

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