Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings - Report Example

Article Summary Definition of Terms EEG stands for electroencephalogram. This is detecting brain impulses by means of electrodes. The electrodes are placed on the scalp of the head to detect the impulses. [Electrodes are metal plates and brain impulses are in form of electric impulses. Brain cells communicate by use of these impulses. Communication is always on even when a person is resting or sleeping] (Mayo Clinic, 2013). Cz this one of the many positions where an electrode can be placed in order to detect brain impulse. This position is right at the middle of the scalp – check the diagram below (BCI, 2000). MNI coordinates – this is an imaging method developed at the Montreal Neurological Institute. It is more of a (X,Y, Z) systems such that using this system can enable a person to exactly point out the position being referred to. For instance the coordinate (15, -48, 60) on the MNI coordinate system is a point just below the Cz. fMRI stands for Functional magnetic resonance imaging and is a technique used to detect flow of oxygenated blood in the blood. Parts of the brain which are busier are likely to receive more oxygenated blood (Devlin, 2013). Power law rule – this is a formula used in neurological studies to get the pitch of an EEG signal Pitch = algAmp + b Fechner’s law – a law which states that “the magnitude of a subjective sensation increases proportional to the logarithm of the stimulus intensity” (Dictionary, 2013, p. 1). Introduction This article is authored by Jing Lu, Dan Wu, Hua Yang, Cheng Luo, Chaoyi Li and Dezhong Yao. Lu et al. (2012) undertook a research in which they developed a unique way of developing physiological music from EEG-fMRI signals. The authors believed this study was important because of the scientific recognition of the attachment of emotional expression and communication with human health. Therefore, finding an efficient way of translating EEG signals to music by ensuring that such music would come out smoothly was be a great step towards enhancing the health of mankind. It was with this noble intention that Lu et al. (2012) conducted this research. The Procedure Followed The research adhered to the expected research ethics of conducting a study. The uniqueness of this study in translating EEG signals to music was in the recording of fMRI signals at the same time as EEG signals were being recorded and from the same signal epicenter. Thus what was obtained was actually EEG-fMRI music. fMRI records were deemed necessary to compensate for EEG music intensity. This approach was necessary to obtain scale-free brain-wave music which is advancement in the reflection of physiological processes of the brain under study (Lu et al., 2012). Two female subjects were used to obtain the EEG-fMRI signal data; one was 31 years old and the other 14 years old. At resting positions, the subjects were scanned. Using a REST software, EEG data was taken after being zeroed. Cz electrode for EEG signals was preferred because its positioning offered insulation against effects of physical changes in the body of the subjects. In order to obtain a simultaneous fMRI recording, fMRI signal was taken from the MNI. The MNI is just below Cz and therefore safe assumption was made that the fMRI and EEG signals would emanate from a similar neural point (Lu et al., 2012). To study the brain music, Lu et al. (2012) concentrated on the pitch and intensity characteristics of a music note. In examining the pitch of the brain music, the power law was applied; this was only to be applied for EEG music which was of course created from the EEG signals. For the intensity of music, the Fechner’s law was applied. In this case both the EEG and fMRI signals were subjected to the Fechner’s law. This was the defining part of the Lu et al. (2012) research. By applying the Fechner’s law to the fMRI signals, the intensity EEG-fMRI music would compensate for the intensity of EEG music. It should be noted that this would be possible because the intensity of EEG-fMRI music follows the power law just as fMRI does. To be clearer, the intensity of EEG-fMRI music was based on fMRI while that of EEG music was based on EEG (Lu et al., 2012). The Findings The findings were that EEG music was obtained from EEG recordings while the EEG-fMRI music was obtained from EE-fMRI recordings. It was found that for the case of EEG music, the pitch and intensity had a significant correlation while in that of EEG-fMRI music, the pitch and intensity did not display a direct correlation. The correlation coefficient for the later was smaller than 0.01; this was noted to be a characteristic of man-made music. Using figures, the scores of EEG-fMRI music and that of EEG music for the two subjects were recorded. On figures, the scores appeared similar but a professional ear could note the difference (Lu et al., 2012). Using well trained musicians, Lu et al. (2012) observed that the intensity of EEG music changed very quickly while that of EEG-fMRI was very slow. It was further noted that “the average identification of EEG music was 85% and average identification of EEg-fMRI music was 90%” (Lu et al. 2012, p. 4). Furthermore, the t-test for the two music was taken - “for EEG music the score was T=2.30, P Read More