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Abstract

The article provides an overview of Brain Computer Interface (BCI) solutions for intelligent buildings. A significant topic from the smart cities point of view. That solution could be implemented as one of the human-building interfaces. The authors presented an analysis of the use of BCI in specific building systems. The article presents an analysis of BCI solutions in the context of controlling devices/systems included in the Building Management System (BMS). The Article confirms the possibility of using this method of communication between the user and the building’s central unit. Despite many confirmations of repeatable device inspections, the article presents the challenges faced by the commercialization of the solution in buildings.
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Authors and Affiliations

Bartłomiej Kawa
1
ORCID: ORCID
Piotr Borkowski
1
ORCID: ORCID
Michał Rodak
1
ORCID: ORCID

  1. Lodz University of Technology, Poland
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Abstract

Brain-computer interface (BCI) is a device which allows paralyzed people to navigate a robot, prosthesis or wheelchair using only their own brains reactions. By creating a direct communication pathway between the human brain and a machine, without muscles contractions or activity from within the peripheral nervous system, BCI makes mapping persons intentions onto directive signals possible. One of the most commonly utilized phenomena in BCI is steady-state visually evoked potentials (SSVEP). If subject focuses attention on the flashing stimulus (with specified frequency) presented on the computer screen, a signal of the same frequency will appear in his or hers visual cortex and from there it can be measured. When there is more than one stimulus on the screen (each flashing with a different frequency) then based on the outcomes of the signal analysis we can predict at which of these objects (e.g., rectangles) subject was/is looking at that particular moment. Proper preprocessing steps have taken place in order to obtain maximally accurate stimuli recognition (as the specific frequency). In the current article, we compared various preprocessing and processing methods for BCI purposes. Combinations of spatial and temporal filtration methods and the proceeding blind source separation (BSS) were evaluated in terms of the resulting decoding accuracy. Canonical-correlation analysis (CCA) to signals classification was used.

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Authors and Affiliations

Marcin Jukiewicz
Mikołaj Buchwald
Anna Cysewska-Sobusiak

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