Statistical Comparison of Delta Band in Two Different Mental States Using MINITAB Tool for Feature Identification
Abstract
Brainwave is unique in its pattern for every individual wherein its spectral components and power show variations with the varying mental states. Ongoing attempt to recognize activity inside the brain has led an integrated research to an unprecedented level. Spectral range of brainwaves vary from 0 Hz to 60 Hz with the amplitude normally between 0.5 µV and 100 µV. Delta waves range from 0.1 Hz to 3 Hz of the whole spectrum generated by the brain and correspond to the deep and dreamless mental state. Brainwaves captured during any mental state comprises of every band while only one band dominates during its corresponding mental state in terms of amplitude and frequency. Research hereby attempts for statistical comparison of Delta band obtained during two different mental states pertaining to normal (thinking nothing in particular) and active (doing mental activity of adding the consecutive integers). For capturing electroencephalogram (EEG) signals, B3 band EEG sensor (Neurosky Product) has been used wherein the readings are recorded by connecting the sensor with the mobile application EEGID (Recording interval-10 msec and sampling frequency-512 Hz) through Bluetooth. Subjects chosen were of the age group 20–30 years for the experiment and were made to sit alone in a room separately for efficient recording.
Hypothesis test named 2-sample standard deviation was applied on two groups of data corresponding to each state. Difference in standard deviations generated among both the states with a cutoff range was the result. This mathematical difference value can be used as a base to devise devices that can recognize various mental states.
Keywords: MINITAB, EEG samples, EEGID, α-level, p-value
Cite this Article
Trivedi KR, Thakker RA. Statistical Comparison of Delta Band in Two Different Mental States using MINITAB Tool for Feature Identification. Research & Reviews: Journal of Computational Biology. 2016; 5(3): 22–27p.
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