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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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Association Attack with Hashcat in a Distributed Environment
Wagner, Lukáš ; Veselý, Vladimír (referee) ; Hranický, Radek (advisor)
The Fitcrack project is a distributed system for cracking cryptographic hashes developed at FIT BUT. The Hashcat tool is used to crack passwords on the computational units. This tool added a new attack mode in 2020 called an association attack. This attack is based on knowledge of a likely password, which is extensively modified during the attack. The goal of this work is to design and implement an extension to the Fitcrack project, which enables the use of the association attack and solves its workload distribution in this distributed environment. Association attack requires modification of distribution methods used by other attacks. Such new methods are proposed and implemented. Implementation is later experimentally verified and conclusion is drawn.
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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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