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The paper presents a process of stimuli design for SSVEP-based brain computer-interface. A brain computer-interface can be used in direct communication between a brain and a computer, without using muscles. This device is useful for paralyzed people to communicate with the surrounding environment. Design process should provide high accuracy recognition of presented stimuli and high user comfort. It is widely known how to make stimuli for BCI which are using high-grade EEG. Over recent years cheaper EEGs are becoming more and more popular, for example OpenBCI, which uses ADS1299 amplifier. In this article we review past works of other authors and compare it with our results, obtained using EEG mentioned before. We try to confirm that it is possible to use successfully OpenBCI in BCI projects.

Kooi and Tamminga's correspondence analysis is a technique for designing proof systems, mostly, natural deduction and sequent systems. In this paper it is used to generate sequent calculi with invertible rules, whose only branching rule is the rule of cut. The calculi pertain to classical propositional logic and any of its fragments that may be obtained from adding a set (sets) of rules characterizing a two-argument Boolean function(s) to the negation fragment of classical propositional logic. The properties of soundness and completeness of the calculi are demonstrated. The proof of completeness is conducted by Kalmár's method. Most of the presented sequent-calculus rules have been obtained automatically, by a rule-generating algorithm implemented in Python. Correctness of the algorithm is demonstrated. This automated approach allowed us to analyse thousands of possible rules' schemes, hundreds of rules corresponding to Boolean functions, and to nd dozens of those invertible. Interestingly, the analysis revealed that the presented proof-theoretic framework provides a syntactic characteristics of such an important semantic property as functional completeness.

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