EEG based stress recognition system based on Indian classical music

2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) IMS Engineering College, Ghaziabad, India

EEG based Stress Recognition System based on Indian Classical Music Ram K. Nawasalkar

Swapnil G. Deshpande

Dept. of Comp. Science, Arts, Comm. and Science College, Kiran Nagar, Amravati (M.S.). India [email protected]

Dept. of Comp. Science, Arts, Comm. and Science College, Kiran Nagar, Amravati (M.S.). India [email protected]

Pradeep K. Butey

V.M. Thakare

HOD, Dept. of Computer Science, Kamla Nehru Mahavidyalaya Nagpur, India. [email protected]

HOD, P.G. Dept. of Computer Science, S.G.B. Amravati University, Amravati, India. [email protected]

For capturing the emotions or mood of the subject, listen the Raga which is the concept present in North Indian Classical music.

Abstract— Emotion and stress plays a significant role in day to day life. Stress arise many complicated medical situation. The aim of this study is to create a new fusion of EEG signals for emotional stress recognition and North Indian Classical Music. In this paper, proposed a method which extracts the EEG signals with the help of scalp of the brain in responding to various stimuli, and recognize the basic emotion like Happy, anger, sad and fear. The EEG signal feature is extracted by using the method of Kernel Density Estimation and emotions can be recognized by using the Multilayer Perceptron. This method visualizes the stress perception during the listening of Raga and neural network classifiers obtained an accuracy of emotion on the flow of valence of arousal model.

In studying neural network, under the emotion perception, use the raga present in Indian classical music for invoking the emotions. In Indian classical music theory, each Raga has its own, emotional states, but small investigation validates the theory, in response of listening the different Raga’s. For identification of positive and negative emotions, take listeners with and without musical training, for selected Raga’s. In our world a major health problem is stress. The main aim of the researchers is to observe a stress signal in a real world environment.

Keywords— Kernel Density Estimation (KDE), Raga, Multilayer Perceptron(MLP), Electroencephalography (EEG)

A density estimation is done by using kernel smoothing method after extracting signal features. To cover the whole data range of x, density was evaluated at equally spaced points. The probability kernel density estimation functions as shown in given below.

I. INTRODUCTION Interaction between user and multimedia devices was done in the form of Music listening. Music listening is a establishment of a direct pathway between brain processes and multimedia interfaces [1]. Brain activity is reflected in the form of Electroencephalogram (EEG) for biomedical research and clinical diagnosis. During the activity of brain cells the frequency range of brainwave is 1 to 100Hz.

In above equation Φ is the standard normal density function and h>0 is a smoothing parameter called as bandwidth. This bandwidth is used for choosing the data points with data overlapping .

Emotion is an important aspect of human life. In learning, perception, creativity, decision making, perception and cognition, emotion plays a vital role. Also, it plays a major role in HCI and affective computing. These emotions may used for finding the mental stress. Mental disorders are also identified by using emotions. There are different cells present in the human brain for different functions. Different methods and ways are present for emotion identification. 978-1-4673-6911-4/15/$31.00©2015 IEEE

KDE can be implemented in any number of dimensions, though in practice, the curse of dimensionality causes its performance to degrade in larger dimensions. MLP is a one of the feedforward artificial neural network model, which has been used for mapping the input data to output data. MLP consists of multiple layers, and each layer is 936


connected with next layer in the network. Each node in the network act as a neuron along with non linear activation function. To obtain the weight of the network, neural network uses the back propagation learning algorithm. Each layer in the neural network and the output of each hidden neuron are distributed to all the neuron [2].

Multilayer Perceptron In this classification model EEG signal is used. It is divided into frequency sub band by using discrete wavelet transform (DWT). The K-means algorithm of each frequency sub-band is clustered by using wavelet coefficients. The performance analysis is done on five different experiments and also improved the classification accuracy rate. These experiments are present in the proposed model of different collection of healthy segments [8].

Music After listening of Indian Classical music person reacts differently. But feature extraction of Raga is an important task. Because an investigation is carried out for collection of features which are significant change the emotion for an individual. These features are also associated with mood or emotion or feelings[11,12,13,14].

In this framework EEG classification is also carried out by using window based average power method. The rectangular approximation is carried out by using frequency sub-bands of the signals. Multilayer perceptron is also compared with adaptive neuro-fuzzy inference system and evaluated the performances. The result of proposed approach is satisfactory with reference to classification accuracy rate [9].

II. RELATED WORK Under the familiarity parameter, investigation of time – course of discrimination among EEG responses and musical appraisal is done. The number of self reported rates of liking was present while listening of music [1].

Emotional Model A number of promising approaches are present in emotion recognition. But in this paper, dimensional approach emotions are described independently along with independent factors. Emotions are described in two dimensional space of valence and arousal. In the above approach, cognitive aspect of emotion in affective space model, and analyzing the brain waves dynamically.

Stress It describes the identification of stress related emotional state using passive single-switch BCI, this work in background of Main BCI. Current mental state of the user has extracted from stress mode. In alpha band, computation of the index of asymmetry indicates the activity in frontal hemisphere. Therefore the stress level was computed by using alpha rhythms in EEG signals [4].

Two dimensions of affective space describe by fundamental model in emotion research. These two dimensions emotional valance and arousal indicating positive vs. negative value, the emotional intensity (from low to high) respectively [3].

It is a proposed system, based on genetic algorithm, support vector machine and artificial neural network. In response of physical and physiological sensor Stress signal can be analyzed over the time [5].

Fig. 1. Emotional Model

In this framework stress has understand and analyze by using Electroencephalogram (EEG) signal. This signal was extracted from scalp for different stimuli are investigated. Only four emotions are considered and using the Kernel Density Estimation (KDE) features are extracted from EEG. The classification is done by using the Multilayer Perceptron (MLP) [6]. EEG The emotion classification algorithm was studied by using electroencephalography (EEG). Participants are expert in emotional music. Four different emotion categories are present, i.e. pleasure, sadness, joy, angry. Training multilayer perceptron classifier (MLP) is used for extraction of alpha power indices of brain activation as a feature vector. Measure the classification accuracy up to 69.69% of four categories of emotion [7,10].

III. METHOD Participants High stress volunteered and healthy volunteered are considered for this study with different fields. These subjects age ranges from 20 to 40 years. In this experiment neurological disorders subjects had eliminated, only normal or corrected vision subjects are taken.

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Fig. 2. Graphical Representation Of Emotions

Data collection With the consent of participants, laboratory tasks are carried out in a dimly light room. Participants were in relaxed situation with closed eyes; EEG was recorded. At that time participant listen a Raga carefully. From the scalp, the electrical brain signal was captured by using EEG. The recording task was taken 1 min for each participant. EEG signals were recorded by using 8 electrodes with reference to earlobes with fixed sample rate. Data processing For off-line analysis, a raw EEG signal of each participant was collected. To eliminate EEG signals drifting and EMG disturbances, process EEG signal by low-pass filtered with 40 Hz cutoff frequency.

The following table 2 shows the performance, accuracy, when valence and Arousal were tested, separated of same subject emotion into MLP. From the above analysis, the result shows the improved accuracy of 95.36%. If the arousal and valance combined together and tested, the accuracy level is obtained.

After reducing the sample rate of signal to 83.33Hz, the signal was normalized with specific length and decimated with factor 3. This decimated data submitted to Kernel Density Estimation for feature extraction. This method was used to extract the feature from sampled data with specific window size and overlapping data.

Testing Arousal and Valence separately, the accuracy level of emotion in % as shown in following table 2. Table 2: The accuracy level of emotion if Arousal and Valance is separated Emotion Recognition

In each frame signal, feature points are estimated through data signal. The features mean and standard deviation of KDE is used for submitting to MLP classifier for classification of emotion. IV. RESULT AND DISCUSSION Emotion Identification In this proposed analysis, emotions were tested in dimensional approach in consideration of arousal and valance. Each subject of performance accuracy was calculated for all emotions. Following table 1 shows the performance accuracy, when valence and Arousal were combined of one subject emotion into MLP.

Emotion Expected in % Emotion

Happy

Anger

Sad

Fear

Happy

91.77

88.65

1.6

1.45

Anger

99.63

45.7

92.6

96.4

Sad

4.79

86.36

87.4

8.5

Fear

0

89.36

1.23

88.6

The graphical representation of four distinct emotions of 4 subjects as shown in following fig 3. Fig. 3: Graphical Representation of Emotions

Emotion Recognition

TABLE I: THE ACCURACY LEVEL OF EMOTION IF AROUSAL & VALANCE IS COMBINED Emotion Expected in % Emotion

Happy

Anger

Sad

Fear

Happy

72.56

98.25

2.85

0

Anger

73.21

12.36

88.65

98.3

Sad

1.36

92.85

73

1.36

Fear

1.38

0

11.2

95.36

Stress Analysis based on Arousal and Valence Emotional change of subject was traced during the listening of Raga. This event is arranged for subject to induce stress. In the above study shows, the dynamic movement of stress by representing the flow of valence of arousal. During listening the classical music, emotion was vary, and moves from Arousal quadrant to Valence quadrants or vice versa.

The graphical representation of four distinct emotions of 4 subjects, when valence and Arousal were combined as shown in following fig 2.

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2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) IMS Engineering College, Ghaziabad, India [11] R. Cabredo, R. Legaspi, S.I. Paul and M. Numao, “An Emotion Model For Music Using Brain Waves,” 13th International Society for Music Information Retrieval Conference ISMIR 2012. [12] A. Gabrielsson, P.N. Juslin, “Emotional expression in music,” In R. J. Davidson, K. R. Scherer, and H. H. Goldsmith, editors, Handbook of affective sciences, New York: Oxford University Press, pp. 503-534, 2003 [13] P.N. Juslin, J.A. Sloboda, “Handbook of music and emotion: theory, research, applications,” Oxford University Press, 2010 [14] E. Schubert, “Affective, Evaluative, and Collative Responses to Hated and Loved Music,” Psychology of Aesthetics Creativity and the Arts, Vol. 4, No. 1, pp. 36–46, 2010.pp. 36–46, 2010.

Arousal and Valence also turned into positive to negative and negative to positive. CONCLUSION To understand the human stress, EEG signals are captured for analyzing basic emotions. The conducted experiment shows that, emotion performance, accuracy while combined arousal and valance provides better accuracy (95.36%) than separate arousal and valence (91.77%). From above experiment, it shows that EEG can be adopted as a promising method to identify and monitor the stress in the human being. Also reduced stress had been monitored during the listening of Raga. In future, this study will be carried out with different classifier for improving the emotion performance accuracy. Also take different raga for this study. REFERENCES [1]

K.H. Stelios amd J.H. Leontios, “EEG-Based Classification of Music Appraisal Responses Using Time- frequency Analysis and Familiarity Ratings,” IEEE Transactions on Affective Computing, Vol. 4, No. 2, April-June 2013. [2] S.R. Kazi, A. Wahab, K. Norhaslinda and M. Hariyati, “EEG Analysis for understanding stress based on Affective Model Basis Function,” 978-1-61284-842-6/11, IEEE 15th International Symposium on Consumer Electronics, pp 592-597, 2011. [3] B. B. Benny, “A two-dimensional affective space: Valence and arousal effects in word processing,” The BAWL databases in research on emotional word processing, 2011. [4] A.C. Atencio, J.C. Garcia, A.B. Benevides, B.B. Longo, “Methodology for analysis of stress level based on asymmetry patterns of alpha rhythms in EEG signals,” Print ISBN: 978-1-4799-5688-3, DOI: 10.1109/BRC.2014.6880974, Biosignals and Biorobotics Conference : Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIPIEEE, 2014 [5] N. Sharma, T. Gedeon, “Modeling a stress signal,” Journal Applied Soft Computing archive, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, The Netherlands ,Volume 14, Pages 53-61, January 2014. [6] K.S. Rahnuma, A. Wahab, N. Kamaruddin, H. Majid, "EEG analysis for understanding stress based on affective Model Basis Function,” 15th International Symposium on Consumer Electronics, doi: 10.1109/ISCE.2011.5973899, pp.592,597, 14-17 June 2011. [7] Yuan-Pin Lin, Chi-Hong Wang, Tien-Lin Wu, Shyh-Kang Jeng, JyhHorng Chen, "Multilayer perceptron for EEG signal classification during listening to emotional music," TENCON 2007-2007 IEEE Region 10 Conference, doi: 10.1109/TENCON.2007.4428831, pp.1,3, Oct. 30 2007. [8] U. Orhana, M. Hekima and M. Ozerb, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Systems with Applications, Pages 13475–13481, Volume 38, Issue 10, 15 September 2011. [9] M. Hekim, “Ann- based classification of EEG signals using the average power based on rectangle approximation window,” Przeglad Elektrotechniczny (Electrical Review), ISSN 0033-2097, Page No. 210215,R. 88 NR 8/ 2012. [10] J. Preethi, M. Sreeshakthy, A.Dhilipan, “A Survey on EEG Based Emotion Analysis using various Feature Extraction Techniques,” International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 11, November 2014.

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