Most biological signals, whether they are muscle signals, brain signals or voice commands, are
random and unending in their initial form. They are processed and filtered from the noise in the
first stage and then classified, analyzed and extracted the required features of it in the second stage.
In this study, the brain signals of a healthy person were captured according to a clinical protocol
prepared for the purpose of the study, which is two consecutive blinks separated by 5 seconds rest
and then a set of digital filters were used to analyze and classify these signals in order to obtain
control from them.
As a result, it was found that the use of Fourier transform is not feasible with biological signals
because it is not cyclical signals. The use of short time Fourier transform can be a solution to this,
but it is flawed by the loss of some frequency samples due to the adoption of fixed window technology
and then the use of wavelet transform which depends on the variable window, but it has a large
number of out-of-band frequencies. Ultimately, the intermittent discrete wavelet transform was
used which provided the most accurate results and we were able to issue the appropriate control