| Περίληψη: | In recent years, the development of low-cost, non-invasive and portable electrophysiological
systems that record and process brain signals has increased. As a result, Brain Computer
Interface (BCI) systems are becoming more accessible to the academic community and the
general public, serving different applications and needs, unlike previous years that these
systems were much more expensive, more complex in their use, and their application was
exclusively in health applications. In this diploma thesis, the goal is to create a BCI system
by which a user can control applications and devices through the signals collected from the
brain and interact with its environment.
At the beginning, the wider field is presented, describing the brain structure, ways of
recording information from the brain, BCI categories and the reasons they succeed. Then,
the general architectural model of BCI systems is presented. There is information on all the
steps that need to be followed in order to create a BCI system as well as many bibliographic
references widely used for various kinds of approaches. More specifically, ways of recording
the data from the brain according to the targeting of each experiment are being presented,
and ways of processing the received signals in order to get rid of noise and strengthen their
informational content are being analyzed. There is, also, a bibliographic presentation of
methods, that are being presented, by which the features are extracted from the processed
signals, aiming at reducing their dimensions and increasing their informational content.
Then all this processed data passes through algorithms and machine learning techniques, to
produce the final model.
In this thesis, a BCI P300-based Speller system is proposed. Emotiv EPOC was the
EEG headset used for the experiments. This study aims to describe the design of a real-time
EEG-based communication aid system, using brain-computer interface technologies. In
more detail, the proposed system consists of a 6x6 matrix display, containing letters and
numbers for the spelling procedure. After the spelling is done, the command is driven to
a Raspberry PI which connects to all the devices and carries a camera with 2 degrees of
freedom combined with computer vision algorithms for the processing. For the speller, an
xDAWN spatial filtering is introduced and different classification methods are compared, in
order to produce the most accurate and fast system.
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