A Study of the effect of sound on EEG

Transcription

1 International Journal of Electronics and Computer Science Engineering 88 Available Online at ISSN A Study of the effect of sound on EEG Renu Bhoria, Faculty Member, ECE Deptt., PIET, Panipat, renubhoria@gmail.com Swati Gupta, Faculty Member, ECE Deptt., PIET, Panipat, swatigupta13@gmail.com Abstract- This paper presents a brief study of various effects of sound on the human brain activity. This can be shown through the study of EEG signal recorded. The effect is in the form of variation in either frequency or in the power of different EEG bands. A biomedical signal electroencephalography (EEG) reflects the state of mind and is often used to verify the influence of music on human brain activity. In fact EEG signals are related to the characteristic parameters of brain electrical activity. Moreover as our mind state changes EEG changes accordingly. The raw EEG cannot be observed or used efficiently. Hence various techniques like time frequency analysis has been employed to read the effects. Index Terms biomedical signal, EEG A 1. INTRODUCTION common method to measure human brain activity is by using Electroencephalography. Electroencephalography is a medical imaging technique that reads scalp electrical activity generated by brain structures. Electroencephalographic measurements are commonly used in medical and research areas. When brain cells (neurons) are activated, local current flows are produced. Only large populations of active neurons can generate electrical activity recordable on the head surface [1]. EEG measures mostly the currents that flow during synaptic excitations of the dendrites of many pyramidal neurons in the cerebral cortex. Differences of electrical potentials are caused by summed postsynaptic graded potentials from pyramidal cells that create electrical dipoles between soma (body of neuron) and apical dendrites (neural branches). EEG signal consists of a wave that varies in time, much like a sound signal, or a vibration. As such, it contains a great deal of information that can be used to characterize the EEG signals for clinical and research purposes. The useful information contained in the raw EEG signal cannot be visualized with just bare eyes. Raw EEG signals usually contain artifacts that will complicate the analysis of EEG signal. These interference waveforms, the artifacts, are any recorded electrical potentials not originated in brain. There are four main sources of artifacts emission: 1. EEG equipment. 2. Electrical interference external to the subject and recording system. 3. The leads and the electrodes. 4. The subject s normal electrical activity from the heart, eye blinking, eyes movement, and muscles etc. in general. 2. EEG ACQUISITION EEG signal acquisition is very important requirement in biomedical engineering for signal analysis [1]. EEG is one of the most important tools in identifying the state of consciousness of a person. Signal acquisition is done by using recording electrodes appropriately placed on the head/scalp. Several different recording reference electrode placements are mentioned in the literature. In our experiments, the System of electrode placement [figure1] is employed, which is based on the relationship between the location of an electrode and the underlying area of cerebral cortex (the "10" and "20" refer to the 10% or 20% inter electrode distance). Electrode placements are labeled according to adjacent brain areas: F (frontal), C (central), T (temporal), P (posterior), and O (occipital). The letters are accompanied by odd numbers at the left side of the head and with even numbers on the right side (Figure 4). Left and right side is considered by convention from point of view of a subject. To visualize these weak electrical signals detected by the scalp electrodes, they are massively amplified, and then displayed on paper or stored to computer memory or other suitable instrument. Encephalographic measurements employ recording system consisting of following parts: (i) Electrodes with conductive media (ii) Amplifiers with filters (iii) A/D converter (iv) Recording device.

2 A Study of the effect of sound on EEG 89 Figure1: Labels for points according to electrode placement system The use of this technology depends strongly on the electrodes positioning and the electrodes contact. For this reason, electrodes are usually constructed from conductive materials, such us gold or silver chloride, with an approximate diameter of 1 cm, and subjects must also use a conductive gel on the scalp to maintain an acceptable signal to noise ratio Figure 1 depicts the System for electrode placement. Practically recorded raw EEG signal Figure 2: Shows recorded Raw EEG signal III. EEG WAVES CLASSIFICATION EEG measures brainwaves of different frequencies present within the brain [2]. The analysis of continuous EEG signals or brain waves is complex, due to the large amount of information received from every electrode. The most widely used method of EEG analysis has been decomposition into different frequency bands based on the Fourier Transform. EEG consists of mainly four basic frequency components namely: (a) Delta Hz - associated with the deep sleep (b) Theta Hz - associated with drowsiness (c) Alpha Hz - associated with relaxed, alert state of consciousness (d) Beta associated with active, busy or anxious thinking

3 IJECSE, Volume 2, Number 1 D GEETHA and T SARIKA 90 Figure3: Shows various EEG wave patterns Although none of these waves is ever emitted alone, the state of consciousness of the individuals may make one frequency range more pronounced than other. The amplitudes of the EEG signals typically vary between 10 and 100 µv (in adults more commonly between 10 and 50 µv).the electrical activity goes on continuously in every living human s brain. We may sleep one third of our life times, but the brain never rests. Even when one is unconscious the brain remains active. Much of the time, the brain waves are irregular and no general pattern can be observed. Figure2 represents various EEG wave patterns. IV. RESPONSE TO MUSIC A lot of research studies have shown that music has physiological effect which is quantifiable. Some research makes use of EEG to verify the influence of music on human brain activity. Frequency distribution analysis and the Independent component analysis (ICA) has been employed to analyze to discover the EEG responses of subjects with different musical signal stimuli [4]. Musical signal stimuli used: 1. metal music 2. sonata music 3. favorite music selected by subjects. Spectral analyses based on Fourier transform were applied to obtain the α, β, δ and θ frequency bands under different music stimuli. The power at each band of each channel was used as the features of EEG. The correlation of the features between different situations and subjects was used to show which channel display the difference of EEG signals. Besides, ICA was applied in the process of isolating noise components and to provide cues to explain the functions of different brain areas in point of neurology. Also it has been demonstrated that T3 and Pz were the channels with relatively lower correlation under different music stimuli. Therefore, it was observed that the locations of T3 and Pz of brain may play an important role in feeling music. V. EFFECT OF NASYID AND ROCK MUSIC One more analyses have been done to study the effect of music on brain activity. This time type of music employed: (i) Nasyid music (ii) Rock music In this study only alpha wave has been observed. Using EEG, the brainwave signal of the sample was captured twice, once before listening to the music and while listening to the music. Consequently, the brainwave's signal was analyzed and the comparison between these two music genres was studied [5]. It has been demonstrated that 60-80% of the samples showed improvement in the alpha band after listening to nasyid while only 56-66% improved after listening to rock music. The maximum value increased in alpha wave was µV and minimum value decreased was 0.755µV. It showed that some of the samples have been distracted and are not relaxed when listening to the rock music especially frontal muscle. Rock music selection does not cause to be relaxed on frontal muscle. These findings indicated that the alpha power increased when listening to the nasyid music as compared to the rock music. Consequently, the results of the whole analysis showed that nasyid music in particular can result in a more relaxing condition as compared to the rock music.

4 A Study of the effect of sound on EEG 91 VI. RESPONSE OF EEG TO NOISE In today s life due to the advancement in technology noise pollution has increased and without realize, this excessive level of noise has lead to the potential health effect. Noise is defined as a sound which is commonly referred to as unwanted sound or meaningless sound of greater than usual volume. In our daily s surrounding, noise coming from various source such as traffic, machinery, industries and electronics. In a study the effect of noise on the fast brain activity using Electroencephalogram (EEG) has been analyzed. The study is aimed to find the correlation between noise exposures to the development of stress and investigated whether it affect the fast brain activity of human. Recordings of EEG signals were divided into two parts: (i) while subjects performed IQ test without 90 db noise exposure (ii) while subjects performed IQ test with 90 db noise exposure Only two types of brain wave, alpha (8-12 Hz) and beta waves (13-30 Hz) were analyzed. Alpha wave was important as stress indicator meanwhile beta wave was associated with fast brain activity such as analytical problem solving, judgment, decision making and processing information. High magnitude of alpha wave and low magnitude of beta wave that obtained before the noise exposure indicated that subjects were relaxed yet focused. However, when subjects were exposed to the noise, beta waves magnitude increased, which indicated that subjects in alert and agitated. Meanwhile, the decrease of alpha wave magnitude showed the subjects in tense. The results of analysis showed that the mean of PSD of beta waves while answering IQ test question with and without noise exposure are contrast with the alpha waves. It was observed that the mean of beta s PSD during noise exposure is higher than during without noise exposure. Following conclusion was made through the study: (i) noise affected the fast brain activity by increasing the magnitude of beta wave and decreasing the magnitude of alpha wave. (ii) the magnitude of alpha wave can be used as indicator either the person is in stress or not. VI. INFLUENCE OF LFN Low frequency noise is a frequent source of annoyance at home and at works. Humans perceive low-frequency waves through the hearing organs and by vibration sensors distributed over the whole body [1, 7]. Low-frequency noise is perceived as annoying when the acoustic pressure levels exceed the threshold of perception, affecting all internal organs in a human body. The effects of low frequency sound on the changes of morphology of the spectral power density function of EEG signals were studied as a part of the research program f=40hz, Lp=110db HP. The participant would then take place inside the cabin, and the. Participants were exposed to low frequency sound of 40 Hz and the acoustic pressure level Lp = 110 db. The sound generation circuit was switched on and the acoustic pressure level inside the cabin was monitored on an online basis. Experimental data were analyzed by numerical methods. EEG signals were recorded and analyzed using power spectral density function. Of particular interest was the fraction of the leading effect of occurrence and that of the amplitude decrease of the power spectral density function of the EEG signal for the alpha rhythm. The analyses of the power spectral density function of EEG potential yields: (i) the fraction of the leading effect occurrence in the sample(when the brain functions at the frequency of the applied excitation ) (ii) the fraction of the leading effect occurrence depending on the sex of the participants. VII. RESPONSE OF EEG TO MUSIC PITCH VARIATION Several studies have been conducted focusing on the effect of music intensity [4], emotion [4], and rhythm [6] on Electroencephalogram (EEG). Various methods have been proposed to study the effect of music stimulus on EEG. This study is aimed to confirm the variation in evoked potential of Electroencephalogram (EEG) when music at different pitches is given as stimulus. It has been studied that α power decreases with increase in mental activity and is predominant in frontal and occipital regions [6]. Here, factor analysis (FA) and neural network (NN) has been used for analysis. To extract the information from EEG factor analysis has been used here since it is the best proposed method for analyzing data sets involving independent components. Factor analysis has been used 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. The whole experiment was divided into different steps as given: (a) Malayalam semi classical song Sreeragamo... in three different pitches was employed.

5 IJECSE, Volume 2, Number 1 D GEETHA and T SARIKA 92 (b) The EEG data of thirty subjects was taken for the study using RMS EEG 32 Super spec by Recorders & Medicare Systems Pvt. Ltd. (c) The EEG measurement was 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) It was observed that EEG varied with variation in music pitch. The method shown in the figure 4 has been used for analysis. In order to show the effectiveness of the above method, α-wave power of the EEG was examined. Hence whole the study work concluded that the proposed method was able to identify the variation in evoked potential of EEG with pitch and there by insinuated that music pitch variation has an impact on EEG Figure 4. Proposed Method. VIII. CATEGORISATION OF SOUND FREQUENCY USING EEG Human brain activities are expected to be different when exposed to different sound frequencies, and can be shown through EEG signals. Hearing different quality of sound, i.e. loud or soft, high pitch or low pitch, audible or inaudible etc., will have different effects to our brain. This thesis work presented the study of sound frequency characteristic based on Electroencephalography (EEG) signals. The study included feature extraction of the EEG signals with respect to different sound frequencies, covering low frequency (40 Hz), mid-range frequency (5000 Hz), and high frequency (15000 Hz). In this study, EEG signal characterization has been done using Fast Fourier Transform (FFT), moving average filters, and simple artifact filtering with reference EEG data per individual. Based on the characteristics of the EEG signal, the sound frequency can be categorized and identified using the proposed method. A study by [7] demonstrated that the sounds containing enhanced inaudible high-frequency component (HFC) has modulatory effect on human acoustic perception. This study was done by using a multi-disciplinary approach, consisting of behavioral measurements of the comfortable listening level (CLL), psychological measurements of the subjective impression of sound and physiological measurements using EEG. Results from this study had shown that with the increase in the intensity of inaudible HFC leads to a significant increase in CLL, the subjective impression of sound, and greater occipital alpha-eeg signal. However, it was noticed that all these effects had some optimum point, and will not increase further once reached the optimum point. The most prominent effects occurred with an increase of +6 db in the HFC. The study on EEG signal with respect to different frequency sound waves is performed. Human brain activities can be shown through the power spectrum of the EEG signals. Result from the study showed that human brain activities were the highest when the subjects were exposed to extremely high or low frequency sound waves. For midrange frequency sound wave, i.e. 5000Hz, which was around the range of most musical instruments, power spectrum of the EEG signal is the lowest, as this was the common and comfortable frequency range perceived by human being. IX. CONCLUSION

6 A Study of the effect of sound on EEG 93 Human brain activities are affected by different types of external sound stimuli. Sound consists of various parameters like pitch, volume, frequency, loudness (decibels) etc. While changing all these different parameters of sound one by one the human brain also reacts differently. All this analysis can be done by using Electroencephalography technique, in which four different brain wave patterns are there. As these frequency patterns cannot be studied in there raw form, so various filtering methods are used for extracting the values of the signals. To elaborate the study work methods like time frequency analysis, Fast fourier transform, Independent Component Analysis, Power spectrum Analysis etc. are used in different studies. REFERENCES [1] M.Teplan, Fundamentals of EEG Measurement, Institute of Measurement Science, Slovak Academy of Sciences Dúbravská cesta Bratislava, Slovakia, Measurement science review, Volume 2, Section , pp.1-5 [2] Huisheng Lu, Mingshi Wang and Hongqiang Yu, EEG Model and Location in Brain when Enjoying Music, IEEE Conference in Engineering in Medicine and Biology, Shanghai, 2005 [3] W. Lin, H. Chiu and C. Hsu, Discovering EEG Signals Response to Musical Signal Stimuli by Time-Frequency Analysis and Independent Component Analysis, IEEE Conference in Engineering in Medicine and Biology, Shanghai, 2005 [4] Ros Shilawani S. Abdul Kadir, Mohd Hafizi Ghazali, Zunairah Hj. Murat, Mohd Nasir Taib, Husna Abdul Rahman and Siti Armiza Mohd Aris, The Preliminary Study on the Effect of Nasyid Music and Rock Music On Brainwave Signal Using EEG, Faculty of Electrical Engineering University Teknologi MARA (UITM) Shah Alam, Selangor, Malaysia. [5] W.Klimesch, M. Doppelmayr, H. Russegger, T.Pachinger, J. Schwaiger, Induced alpha band power changes in the human EEG and attention, Neurosci. Lett., 244 (1998) [6] Christian uhl, A new concept for eeg/meg signal analysis: detection of interacting spatial modes, max- planck-institute of cognitive neuroscience, d leipzig, germany, human brain mapping 6: ,1998 [7] Louis A. Schmidt and Laurel J. Trainor, Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions, COGNITION AND EMOTION, 2001, 15 (4), [8] 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 [9] E.J. He, H. Yuan, L. Yang, C Sheikholeslami, and B. He, EEG spatiospectral 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 [10] R. Yagi, E. Nishina, M. Honda, T. Oshashi, Modulatory Effect of Inaudible High-Frequency Sounds on Human Acoustic Perception, Neuroscience Letters 351, Ireland: Elsevier, 2003, pp AUTHORS PROFILE Ms. Renu Bhoria is currently Assistant Professor at Panipat Institute of Engineering and Technology, Panipat, India. She has done M.Tech. from DCRUST, Murthal, Haryana, India. Ms. Renu has completed her B.E. (Hons.) from DCRUST, Murthal, India. She has 1 year of academic experience. Ms. Renu has authored 2 research papers in reputed Journals. Her areas of interest include Communication Systems and Wireless Communication. Ms. Swati Gupta is currently Assistant Professor at Panipat Institute of Engineering and Technology, Panipat, India. She has done M.E. from NITTTR, Panjab University, and Chandigarh, India. Ms. Swati has completed her B.E. (Hons.) from Vaish College of Engineering, Rohtak, India. She has 10.5 years of academic experience. Ms. Swati has authored 3 review papers, 2 research papers in reputed National Conferences and 2 research papers in international journals. Her areas of interest include Communication Systems, Wireless and Mobile Communication and Digital Signal Processing.