For the EEG wave patterns, the power spectral density plot calculated from the EEG data recorded at $Oz$ while a participant $($P$2)$ was focusing on the left$-$and right$-$sided visual stimuli for $10s$ in the offline experiments. See the citation below.

Note: This figure has been quoted from $($Study biomedical signals and images, Matlab, and LabView code$).$

Quoted, "The power spectral density plot calculated from the EEG data recorded at $Oz$ while a participant $(P2)$ was focusing on the leftand right$-$sided visual stimuli for $10s$ in the offine experiments. The blue solid line indicates concentration on the left LED channel flickering at $10Hz$ while the red dashed line indicates concentration on the right LED channel flickering at $11Hz".$

Citation: Classification of binary intentions for individuals with impaired oculomotor function: 'Eyes$-$closed' SSVEP$-$based braincomputer interface $($BCI$),$ March $2013$Journal of Neural Engineering $10(2)$:$026021$

DOI: $\frac{10\mathrm{.}1088}{1741}-\frac{2560}{10}\frac{?}{?}\frac{2}{026021}$

Create and fit using the Newton Gregory $($Polynomials a $5^{th})$ and Least Square Error algorithm $($Polynomials a $5^{th})$ order polynomial models for frequency ranges $(12$ and to $20)$ for both EEG power spectrum for both Attend to Left Stimulus and Attend to Right Stimulus brain wave patterns using the Transformation for data Linearization, for any $)=(20$ points for both ranges. The Newton Gregory and Least Square Error algorithm models should be $5^{th}$ order polynomials. $(5$ marks$).$

Write your own code $($any coding platform: c$++,$ matlab, or python$),$ to verify the models and to verify your results. $(5$ marks$)$