Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

The rapid increase of many disorders, such as stroke, amyotrophic lateral sclerosis (ALS) or various other spinal cord injuries, strongly affects the society. This results in growing need for the improvement of communication methods in order to enable quick and efficient interaction with the environment, where in some particularly difficult cases this may be the only possible communication way. Therefore Brain-Computer Interfaces (BCI) seem to be an excellent solution not only for the, above mentioned -severe cases, but also for nondisabled, healthy users. The main purpose for the research presented in this paper was to invent easy, but efficient method for the analysis of the EEG signals and its implementation for the control purpose. As the implementation of EEG signals in BCI systems has become recently more and more popular within the last few years, lots of similar solutions have been developed. The method developed by the authors of this paper presents an innovative approach in analysis of the electroencephalographic signals. The proposed method is novel not only because of its efficiency, but also because of the choice of the applied equipment. The signal processing method was implemented on an embedded platform, so all the limitations of the embedded systems had to be taken into consideration. The proposed solution also enables customisation of the analysing criteria by using a threshold function in order to enable adaptation for various specific applications. In the carried out study only signals with limited information have been processed. The invented method is based on basic mathematical operations only. Neither filtering nor sophisticated signal processing methods were used.