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Analysis of observed chaotic data

Human cognition analysis from EEG signal is one of the most significant way to find the causes which affect human brain. Songs are familiar stimuli to analyze the effectiveness of different EEG bands because it is a common source of entertainment. According to linguistic variation, subject age and preference, volume level of songs, the impact on different EEG bands varies. In this research, a work has been performed to estimate the effect of songs on different EEG bands (delta, theta, alpha, beta). The songs have been categorized into three types: mild, pop, rock according to their PSD value which are 0.2114 W/Hz, 0.3271 W/Hz and 0.8448 W/Hz respectively. the volume levels are considered as 0 %-14 % volume level for low volume, 26%- 60% volume level for comfortable volume and 61%-100% volume level for high volume. An ANOVA test has been conducted to indicate the significance variation in EEG band and in which P-value has been set 0.05. For the mild, pop, and rock songs the P-values for alpha bands are 0.0146, 0.006 and 0.0006 respectively and the values indicate that alpha band is mostly active while listening to different songs. A topographical representation has been done based on the effects of songs on the alpha band activity for mild, pop and rock songs at three volume level. The maximum percentage of alpha band activation is 60% in comfortable volume which indicates that, when the volume is high human cognition state moves from relax to stress condition in which beta band is active.

An Approach to Estimate the Activation of Different Bands of EEG Signal using Classified Songs

Study of the effect of music on autonomic nervous system of human being through the analysis of Heart rate variability (HRV) signals is a very recent area of research. The present paper considers the effect of seven different types of music stimuli on a single subject. The data of the HRV signals are subjected to a recently developed technique known as `frequency-delay plot'. The quantification is done by `ellipsoid fit' and allied five different types of quantification measures. Such results of quantification are then analyzed by the method of 'randomized block ANOVA. It is proved that all the different types of music stimuli have different effects on ANS of the subject, and that the effects are significantly different. Moreover the five different measures also do differ significantly on the music data. Finally to test which music signal affects ANS most, we consider, as population, the totality of all data corresponding to different types of songs and different types of measures taken. By considering the data of individual song under all measures as data of a sample, we calculate the interval of confidence of the population mean and the population variance with the help of each sample mean and sample variance at 0.95 level of confidence. It is proved that the length of confidence interval for population mean as well as for population variance is minimum only in the case of song1. In other words, it means that song1 is the most effective one on ANS of the subject.

Dipan sarkar

Papers

Study of the effect of different music stimuli on autonomic nervous system of a single subject