A Study of Effect of Music Pitch Variation in EEG using Factor Analysis and Neural Networks

Sreedevi M, Ajesh A, Ajithnath R, Binu L.S Dept. of Electronics Engineering

College of Engineering Trivandrum Kerala, India

Abstract—The aim of this study is to confirm the variation in evoked potential of Electroencephalogram (EEG) when music at different pitches is given as stimulus. In this study we used factor analysis to discover the EEG responses of subjects with different musical signal stimuli. It is expected that some features can be demonstrated to reflect the different musical stimuli. After extracting the characteristic data of EEG using Factor Analysis, Neural Network was used to estimate the extracted characteristic data of EEG. It was observed that EEG varied with variation in music pitch. Finally in order to show the effectiveness of the above method, α-wave power of the EEG was examined.

Keywords-EEG; evoked potential; music pitch; alpha wave; neural network; factor analysis

I. INTRODUCTION Several studies have been conducted focusing on the effect

of music intensity [1], emotion [1], and rhythm [2] on Electroencephalogram (EEG). Here the effect of pitch variations is studied. EEG consists of mainly five frequency components namely δ (0 - 4 Hz), θ (4 - 8 Hz), α (8 - 13 Hz), β (13 - 22 Hz) and γ (22 Hz onwards) waves. It has been studied that α power decreases with increase in mental activity and is predominant in frontal and occipital regions [3]. Various methods have been proposed to study the effect of music stimulus on EEG. Here we use factor analysis (FA) and neural network (NN) for analysis.

To extract the information from EEG we used factor analysis since it is the best proposed method for analyzing data sets involving independent components. EEG is a time series signal that has more than one factor intricately intertwined and the EEG contains much noise. Also EEG has the information which is difficult to obtain by the direct observation of data. Finally the neural network is used for EEG analysis. Using neural network we can even establish a non-linear relationship.

Here we use a Malayalam semi classical song ‘Sreeragamo...... ’ in three different pitches. First we establish that the proposed method is correct by proving the known fact that the evoked potential of EEG varies while listening to music [4]. Then we verify the EEG variation with different pitches using the above method.

II. METHODOLOGY

A. Experimental setup The EEG data of thirty subjects are taken for the study.

The EEG is taken using RMS EEG – 32 Superspec by Recorders & Medicare Systems Pvt. Ltd. The acquisition and analysis of the EEG data can be done at the same time using the built in software. An excellent signal to noise (S/N) ratio is obtained using a digital head box (Adapter). The surrounding noise is kept low and subjects are asked to lie down in a relaxed position.

Electrodes are placed according to the 10 – 20 system (as shown in Fig. 1). 16 channel bipolar EEG recording is done for the 30 subjects.

The EEG measurement is done for 3 different pitch scales for the same music. The pitch of the original music is scale B (pitch 1). The other two pitches are scale B shifted down by an octave (12 semitones, pitch 2) and a whole tone (pitch 3).

B. Proposed Method The subjects are asked to lie down in a relaxed manner with minimum eye and muscular movement. Initially the EEG is recorded for four minutes without listening to music. Then music at three different pitches are given, each for four minutes and EEG is recorded.

Figure 1. 10 – 20 system.

978-1-4244-4134-1/09/$25.00 ©2009 IEEE

The structure of the proposed method is shown in Fig. 2.

Figure 2. Proposed Method.

16 channel EEG data of the subjects is taken based on the

international 10-20 system. The sensitivity of the measuring instrument is 7.5µV. A band pass filter (1-70 Hz) is used for filtering the signal. For processing the data, the signal is sampled at a rate of 256 Hz to obtain the digital data. The time series data is then transformed to frequency components using Discrete Fourier transform (DFT).

After taking DFT of the data set, characteristic data extraction technique, Factor Analysis is used. The common factor is extracted by using principal factor analysis method [5]. In this paper we assume that the characteristics data of the EEG is the data of first factor loading. Then the NN is used for estimating the extracted characteristic data of the EEG. Here we use the NN tool box of MATLAB R2007b. The network used is a two layer Perceptron network (Fig. 3). The learning function used is LEARNP and the transfer function is HARDLIM.

Figure 3. Neural Network.

Here the NN is designed to classify the data set into two i.e. only a single neuron is present in the output layer. For each subject we use three different networks for classification. The first one classifies pitch 1 and 2; second one classifies pitch 2 and 3 and third one pitch 1 and 3.

The input to the NN consists of several data sets; each of size 16 X 1. Each data set corresponds to a 5 second duration EEG data and each data in the set corresponds to 16 different channels. The 16 X 1 data set is obtained by FA of the 5 second data set with size 1280 X 16.

Each network is trained with 20 data sets, 10 of each class. If pitch 1 and 2 are the two classes, then 10 data sets of pitch 1 and 10 sets of pitch 2 are taken, each class trained against a target 0 and 1.

III. RESULTS To prove the credibility of the proposed method, the proven

fact that EEG varies while listening to music [4] was verified using the method. This was achieved by designing a network to ascertain the variations in EEG while listening to music and not listening to it. Here the data of 5 subjects out of the 30 are shown. Table I shows the classification accuracy of the network in the above case. From the table it can be seen that an average classification accuracy of about 96 percentages is obtained.

The table is shown below.

TABLE I. WITH MUSIC AND WITHOUT MUSIC

Subjects Classification Accuracy (%)

Subject 1 92

Subject 2 100

Subject 3 98

Subject 4 96

Subject 5 94

Since the classification accuracy is high, the proposed method is able to identify the differences in the evoked potentials. So the proposed method can be used to study the effect of pitch variation in EEG.

The result of classification of pitch 1 and 2 is shown in Table II. From the table the average classification accuracy is found to be 64 percentages.

TABLE II. PITCH 1 AND PITCH 2

Subjects Classification Accuracy (%)

Subject 1 58

Subject 2 60

Subject 3 87

Subject 4 55

Subject 5 60

The result of classification of pitch 1 and 3 is shown in Table III. From the table the average classification accuracy is found to be 86.4 percentages.

TABLE III. PITCH 1 AND PITCH 3

Subjects Classification Accuracy (%)

Subject 1 93

Subject 2 75

Subject 3 70

Subject 4 94

Subject 5 100

The result of classification of pitch 2 and 3 is shown in Table IV. The average classification accuracy is 69.4 percentages.

TABLE IV. PITCH 2 AND PITCH 3

Subjects Classification Accuracy (%)

Subject 1 53

Subject 2 76

Subject 3 88

Subject 4 55

Subject 5 75

From the tables we can see that the networks are able to

classify the three pitches. This indicates that the three pitches have different effects on EEG. To substantiate this α-power in the occipital region for the three pitches is studied.

The alpha power at the electrodes O1 and O2 is as shown below. The powers were obtained using the RMS EEG Analysis software. Table V shows the alpha power at O1 for the three pitches and Table VI the power at O2.

TABLE V. ALPHA WAVE POWER AT O1

Subject Alpha wave power Pitch1 (µV2) Pitch2 (µV2) Pitch3 (µV2)

Subject 1 1.98 0.78 1.48 Subject 2 5.41 3.51 4.40 Subject 3 6.22 4.19 5.51 Subject 4 20.02 12.64 13.82 Subject 5 10.39 5.46 8.53

TABLE VI. ALPHA WAVE POWER AT O2

Subject Alpha wave power Pitch1 (µV2) Pitch2 (µV2) Pitch3 (µV2)

Subject 1 4.17 1.43 2.12 Subject 2 11.08 5.07 8.34 Subject 3 16.01 10.16 10.89 Subject 4 19.97 9.6 12.53 Subject 5 8.43 4.49 7.41

IV. INFERENCE From the tables II, III and IV we can infer that the pattern

of EEG is different for different pitches. In the case of pitch 1 and 2 it is seen that an average classification accuracy of 64% is obtained, while an average of 86.4% is obtained for pitch 1 and 3.

For pitch 2 and 3 the average is obtained to be 69.4%. This high classification accuracy indicates that the networks are able to classify between the pitches. From table V and VI, it is seen that alpha power decreases with decrease in pitch. Alpha power is maximum in the case of pitch 1 i.e. scale B. When the scale is reduced by one whole tone (pitch 3) the alpha power is found to decrease. It further decreases for pitch 2 which is one octave (12 semitones) down. The alpha power variation is predominantly observed in the occipital regions.

Hence it can be concluded that the proposed method is able to identify the variation in evoked potential of EEG with pitch and there by insinuate that music pitch variation has an impact on EEG.

ACKNOWLEDGEMENT With great pleasure we would like to express our immense

gratitude to Dr. (Prof.) Sukesh Kumar, former Dean of College of Engineering, Thiruvananthapuram, for all the guidance given. We would also like to thank Dr. Thomas Iype, professor and head of Department of Neurology, Medical College, Thiruvananthapuram and Mr. Prajith.C.A, Lecturer, College of Engineering, Trivandrum for their continued support and advice throughout the work.

REFERENCES [1] Louis A. Schmidt and Laurel J. Trainor, “Frontal brain electrical activity

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[2] Huisheng Lu, Mingshi Wang, Hongqiang Yu, “EEG model and location in brain when enjoying music,” Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.

[3] E.J. He, H. Yuan, L. Yang, C Sheikholeslami, and B. He, “EEG spatio-spectral mapping during video game play,” Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, in conjunction with The 2nd International Symposium & Summer School on Biomedical and Health Engineering Shenzhen, China, May 30-31, 2008.

[4] Wei-Chih Lin, Hung-Wen Chiu, Chien-Yeh Hsu, “Discovering EEG signals response to musical signal stimuli by time-frequency analysis and independent component analysis,” Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.

[5] Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu, “A feature extraction of the EEG during listening to the music using the factor analysis and neural networks,” 0-7803-7898-9/03/$17.00 02003 IEEE.