The 2014 Biomedical Engineering International Conference (BMEiCON-2014)
EEG-based Bel System via Arithmetic and Emotional Imagery Yunuong Punsawad*
Department of Electrical Engineering Faculty of Engineering and Industrial Technology Silpakorn University, NakhonPathom, Thailand. *
[email protected]
Abstract-Nowadays, the number of severe disabled (a totally dependent or assisted living) is dramatically increased due to the increasing rates of accident and brain disease. Therefore, this paper proposes a novel modality of brain-computer interface (BCI) system as an alternative assistive tool for the severe disabled. The use of EEG based emotional and arithmetic imagery from the pre-frontal area is employed. Linear discriminant analysis (LDA) is used to classify emotional and arithmetic imagery by using spontaneous EEG. The comparison between linear and quadric functions of LDA classifier isproposed. The results of the proposed BCI modality as well as its processing algorithm as LDA with quadric can achieve 86% accuracy function. Hence, this system can be one of the potential assistive tools for the serve disabled in their daily activities.
Keywords-Emotional Imagery, Electroencephalography EEG, Brain Computer Interface BCI
I.
INTRODUCTION
Rate of disability are increasing in Thailand due to accident and health conditions such as Amyotrophic lateral sclerosis (ALS) and Spinal cord injury (SCI). Some patients cannot live alone caused they have no ability to move and communicate. Some can communicate with other and aware of environment; can assume that their brain still functions. Brain Computer Interface (BCI) Technology is employ to help awareness patients to do their daily life. For example, they can switch electrical equipment. They also increase ability to show how they feel or what they want that can improve quality of their life. BCI system is a system that widely use in research and medical applications. It is analyzed the information from a brain with an algorithm in the computer then present on the screen or user interface equipment to show the quantity or the physical of the brain. Electroencephalogram (EEG) is a method that use to measure brain potential and the data will process in the computer. These can increase the way to cure patients or to invent medical equipment. Spontaneous brain potentials are EEG signals are effect from inner examinee state as, e.g. meditation, relaxation, sleeping, calculating, etc. It is not effect Manuscript received August20, 2014. This work is supported by Faculty of Engineering and Industrial Technology, Si1pakom University and Mahido1 University.
978-1-4799-6801-5114/$31.00 ©2014 IEEE
Juthamat Uengamphon,Yodchanan Wongsawat
Department of Biomedical Engineering Faculty of Engineering, Mahidol University NakhonPathom, Thailand.
[email protected]
of some stimulus from outside as, e.g. visual, auditory and sensory stimulus. Spontaneous brain potentials can beclassified according to their frequencies. The delta waves have frequency range from 0.5 hertz (Hz) to 4 Hz. It can be found during sleeping and complex solving. The theta waves have frequency range from 4 Hz to 7.5 Hz. It can be normally seen in sleep and creativity. The alpha waves have frequency range from 8 Hz to 13 Hz. It can be found during eyes closed, relaxation and concentration. The beta waves have frequency range from 14 Hz to 30 Hz. These waves can be measured from the Frontal and Parietal region when the subject are thinking (e.g. mathematics calculating), in addition it has directly relationship with motor cortex system. The gamma waves have frequency range from 25-40 Hz. These waves can be found when the subjects perform the very high mental activities. Beside the EEG rhythms, we can identify the characteristic and function of each location of brain by following 10-20 standard electrodes system. For example, left occipital 01 area is the visual processing of right eye. Right frontal F8 is an emotion expression function. Therefore, the motivation of this work is the utilization a spontaneous EEG of emotional imagery for BCI system. We try to investigate the brain phenomenon during emotional imagery by using quantitative QEEG method. This paper proposes EEG-based BCI system via emotional and arithmetic imagery from pre-frontal area to study the phenomena of frontal area of brain. The invention also develops from result of the study to help severe disability patients. They will tum on the electrical equipment and can communicate with others that can enhance abi1ityto do an activity in dairy living.
Fig. 1. Experiment setup
The previous research of emotional imagery, Kothe, et. al. [4] proposed the EEG base emotion recognition during self paced emotional imagery. They tried to investigate a prediction of emotion by using spontaneous EEG activity. The result reported an average accuracy of 71.3%was achieved. Moreover, Cao et al [9] proposed a study of electrical activity of the brain withchineseemotional wordsfor emotion recognition. They employed support vector machine (SVM) and linear discriminant analysis (LDA) to recognize emotion. However, the result showed a low accuracy that can be achieved. The proposed of evaluation of emotional recognition by using EEG signal from the frontal lobeswas proposed by Tabacaru et al [10]. They used left dominant alpha-asymmetry (LDAA) index to identify the brain state that is the contribution. Research of EEG based arithmetic, Q. Wang and friend study about mental arithmetic by using EEG signal [8]. They focus on a development of the EEG basedneurofeedbacksystem. This is an ideal of alternative way for psychological disordertreatment in case of learning disorder (LD) and autism spectrum disorder (ASD). They proposed methods and contributions that are benefit to our work in part of arithmetic imagery. Normally, thealgorithms of classification are commonly proposed in research on EEG based BCI system such as linear classification; lineardiscriminant analysis (LDA) support vector machine and (SVM) and neural network (NN).LDA is popular method [812]. For example, a two class of commands as left right motor imagery that is a popular modality of BCI system by employing a feature of EEG signal from motor cortex area [11]. Therefore, this work employed LDA to recognize a brain state of arithmetic and emotional Imagery. II.
PROPOSED METHODS
Tasks in the experiment will show on the screen. Pictures about mathematic problem represent logical task and another represent emotional task. In process, subjects are asked to rest, sit without thinking, before start the tasks in 5 seconds or 5000 milliseconds. Then they go on tasks. There are 30 tasks for one experiment. Tasks will appear randomly in 8 seconds or 8000 milliseconds which subjects should think how they feel about that picture before the picture change to another one until the thirtieth pictures (the last one). B.
EEG Recording
According to 10-20 international system, electrodes are posited on Fpl , Fp2, F7, F8 and Cz that become 2 channels of the EEG data. First channel is Fpl (+), F7 (-) and Cz (ground) and another is Fp2 (+), F8 (-) and Cz (ground). This method calls bipolar measurement which can reduce noise from the signal, so the data of the brain from Fp1 and Fp2 positions will be cleaned. Data from Fp 1 show attention of logical thinking and Fp2 show attention of emotional thinking. Biopac™system is employed to measure the EEG signal. Sampling rate of EEG data is 200 Hz.The recording has to control an electrode impedance must be 5- 30 kO . The signal is amplified 50000 time, then it is gone through analog bandpass filter (0.5-35 Hz), after that the signal is converted with analog-to-digital convertor (ADC) to computer for data collection. C.
Signal processing
For EEG signal processing part, EEG signal collection can perform by using the biosignal processing method as digital filter and smoothing method were used. EEG signal is filtered by digital highpass filter at 2 Hz of cutoff frequency for reducing artifacts from time domain conversion. Moreover, digital lowpass filter at 2 Hz were used to filter a noise for collecting only signal in range of EEG bandwidth.Infinite impulse response IIR filter is used for implementation. Fourier transformtheory was employ to convert EEG signal in time domain to frequency domain. The overlap data was 50 of window. Power spectrum with Fourier transform can be calculated by:
1
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wherex (n) is a valve of sampling data of EEG signalfnis fundamental frequency and N is a number of data in one window. D.
Fig. 2 Paradigm of Arithmetic and Emotional Imagery
A.
Emotional Imagery Tasks
Emotional Imagery task is designed by the phenomena of the brain when think about logical task and emotional task.
Parameter Setup and Feature Extraction
This section explains about the data preparing. The EEG signal from Fp1 and Fp2 are employ to provide parameters. The average power spectrum of each EEG rhythms as alpha theta and beta are feature of signal that use to provide the parameter for classification the arithmetic and emotional imagery state by using lineardiscriminant analysis (LDA). There are 6 parameters for one vector. According the EEG recording, the data set can be divided into 2 sets are the training data set and testing data set. Before classification,
LDA need the training data to generate aline of classification as data calibrating. The testing data will feed to the classifier to check a performance and accuracy of proposed system.The parameters can be calculated by:
B. Results
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section. The sample data of each subject is 240 samples. The cross validation is used to perform the sample by separate sample into 4 groups. Each group of data are validate to testing and training data for performance testing.
(2)
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wherePS(fJis power spectrum of each frequencies and n is channel of pre-frontal (Fp) area of brain(n) are I and 2 or left and right side of the brain. Is andJe is the starting frequency and end of frequency in each EEG band frequencies are theta alpha and beta. The EEG signalarithmetic and emotional imagery state are transferred to parameters. The power spectrums of alpha and beta band of each channel are ordered to the set of data. We would like to classify the state of imagery between signalarithmetic and emotional. Normally, the use of spontaneous EEG which is theta alpha and beta is a conventional algorithm for study electrical activity of the brain. Therefore, this work employed the spontaneous EEG with LDA method to create a command for BCI system.
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Fig. 3The example of LDA with Quadratic function reporting
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Linear Discriminant Analysis LDA
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Linear Discrimination Analysis (LDA) [10, 12] is a method of classifier used to categorize two or more of data or events by employing a relationship in mathematic theory such as linear or quadratic combination of data features.Furthermore, in terms of fisher's linear discriminant(FLD) is computeda direction for the projection of data. We can define by this equation:
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Considering to a maximum of the eigenvalue problem (J)where SB is the between classes scatter matrix and Sw is the within classes scatter matrix. This paper would like to compare accuracybetweenlinear and quadratic functions of LDA classifier. Moreover, identification of the EEG band as theta alpha and beta which are related to the arithmetic and emotional imagery state. The position improvement of the logical thinking in left frontal area of the brain and emotional expression in right frontal area of the brain is benefit of the proposed. Most of all, we would like to provide a novel modality of EEG based BCI system for severe case of disability person.
Five volunteer subjects participated in the experiment. The EEG will be recorded while they are doing the tasks both logical task and emotional task. They have to follow the paradigm in the experiment and do 3 sessions of the experiment. The EEG of subjects will be analyzed with the algorithm to find parameters of the task, and then calculate the accuracy of the parameters by following proposed method
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Fp I Emotional training Fp2 Emotional training Fp I Emotional testing Fp2 Emotional testing Fig. 4The example of LDA with Linear function reporting
According to the process of classification, we can reported and accuracy of the proposed by following Table I and Table II. We focus on the EEG band and function of LDA classification. TABLE 1. ACURACY OF EMOTIONAL IMAGERY CLASSIFICATION
III. EXPERIMENT AND RESULTS
A. Experiment
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Emotional Imagery Sub.
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Theta Quadra tic
87 53 68 73
Beta
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Linear
77 60 53 63
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Linear
Quadra tic
53 57 60 68
57 57 60 73
97 83 77 87
Linear
97 83 73 73
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TABLE II.
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ACURACY OF ARITHIMETIC IMAGERY CLASSIFICATION
Arithmetic Imagery Sub.
Theta Quadra tic
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Linear
Quadra tic
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83 87 83
1 00 83 73 80
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68 57 63 63 57
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Linear
DISCUSSION
According to the results, Table I is the accuracy of emotional imagery classification. The beta band of EEG can achieved a high accuracy is 85% with quadratic function. Linear function has 81 % of accuracy. We found the beta band can be use to recognize emotional in imagery state with the frontal lobes of the brain by following the previous work [1 0]. Focusing on Table II, the results are the accuracy of arithmeticimagery classification. Alpha band of EEG can achieved a high average accuracy which is 86% with quadratic function and 83% with linear function for arithmeticimagery classification. Summary, we can improve the location of brain for data acquisition. Moreover, LDA quadratic function can achieve a high performance to recognized brain state of arithmetic and emotional imagery. Beta band can be used for emotional imagery parameter. Alpha band can be used for arithmetic imagery parameter. The example of LDA classification reporting showed in Fig. 3 and 4.The performance of LDA with quadratic function is very high. This can be used in BCI system. Fig.5 is a graphic user interface for EEG based BCI system via arithmetic and emotional imagery. IV.
Fig. 5 GUI of the proposed real time system
ACKNOWLEDGMENT
This work is supported by Faculty of Engineering and Industrial Technology, Silpakom University and Mahidol University, Thailand. REFERENCES [ 1]
J. R. Wolpaw, et aI., "Brain-computer interfaces for communication and control", Clinical Neurophysiology, 2002, vol. 1 13, pp. 767-791.
[2]
G. Pfurtscheller, et aI., "Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-based "Brain Switch:" A Feasibility Study towards a Hybrid BCr', IEEE Trans. on Rehabilitation Engineering, 2010, VoLl8: pp. 409-414.
[3]
J.R. Wolpaw, et aI., "Brain-Computer Interface Technology: A Review of the First International Meeting", IEEE Trans. on Rehabilitation Engineering, 2000, Vol. 8, pp. 164 - 173.
[4]
C. A. Kothe et aI., "Emotion Recognition from EEG during Self-Paced Emotional Imagery," Conference on Affective Computing and Intelligent Interaction CACI!), 20 13, pp.855-858.
[5]
H. Kwon et aI., "EEG Asymmetry Analysis of the Left and Right Brain Activities During Simple versus Complex Arithmetic Learning" Journal of Neurotherapy: Neuroscience, 2009,Vol. 13,pp. 109- 1 16
[6]
F.T. Rocha et aI., "Brain mappings of the arithmetic processing in children and adults", in Cognitive Brain Research 22, 2005, pp. 359-372
[7]
J. Malmivuo, and R. Plonsey, "Electroencephalography", Bioelectromagnetism, Oxford, 1995, pp. 257-264.
[8]
Q. Wang and O. Sourina" Real-Time Mental Arithmetic Task RecognitionFrom EEG Signals", IEEE transactions on neural systems and rehabilitation engineering, vol. 2 1, no. 2, march 20 13 pp. 225-232
[9]
M. Cao et al.,"EEG-based Emotion Recognition in Chinese Emotional Words",Proceedings of IEEE CCIS20ll,pp. 452-456.
CONCLUSION
In this paper, we have proposed the novel modality of EEG based BCI system. Arithmetic and emotional imagery which are metal task to stimulus the inner examinee provide a spontaneous EEG. LDA classifier can be used to categorize state of arithmetic and emotional imagery. According the results, the proposed can be employed to provide a BCI system for serve person with disabilities. For the future work, we would like to do in real time implementation for BCI system.
in
[ 10] B.-A. Tiibiicaru et al.,"Evaluation of Emotional Influence on EEG Activity in the Frontal Lobes" The 4th IEEE International Conference on E-Health and Bioengineering - EHB 2013. [ 1 1] S.-L. Wu et al.," Common Spatial Pattern and Linear Discriminant Analysus for Moter Imgery Classification"IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) 2013, pp. 146- 15 1. [12] F. Aziz et al.," Discrimination Analysis of EEG Signals at Eye Openand Eye Close Condition for ECS Switching System"International Conference on Electrical Infonnation and Communication Technology (EICT),2013.
