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In this paper we consider a system (plant) of “Hudgkin - Huxley classical mathematical model of EEG signals” as an input-output map y = f(x). We assume that the internal structure of this system is unknown, but qualitative knowledge about the behavior is available in the form of "if - then" rules. We construct a mathematical description of the system, based on available information, so that it will represent faithfully the true system of “Tsukamoto Fuzzy Control Model”. The construction process consist of translating linguistic rules into mathematical expression using fuzzy sets and fuzzy logic using the technique of Tsukamoto fuzzy inference rules so that  desired output result is achieved.

Further we generalize this model by making ±10%, ±20% etc. variations in the input sensor readings and achieve the expected output results.

The obtained Tsukamoto fuzzy controlled model is shown to be within the class of designs capable of approximating the true input- output relation to the required degree of accuracy.

2000 Mathematics Subject Classification: Primary: 94C42, Secondary: 68T27, 68T37.

Mathematical model of EEG signals
inputs – output linguistic variables
Tsukamoto fuzzy rule base
weighted average formula.
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