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Body Area Networks. Smart IoT and Big Data for Intelligent Health - 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings (Paperback, 1st ed. 2020)
Muhammad Mahtab Alam, Matti Hamalainen, Lorenzo Mucchi, Imran Khan Niazi, Yannick Le Moullec
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This book constitutes the refereed post-conference proceedings of
the 15th International Conference on Body Area Networks, BodyNets
2020, held in Tallinn, Estonia, in October 2020. The conference was
held virtually due to the COVID-19 pandemic.The 15 papers presented
were selected from 30 submissions and issue new technologies to
provide trustable measuring and communications mechanisms from the
data source to medical health databases. Wireless body area
networks (WBAN) are one major element in this process. Not only
on-body devices but also technologies providing information from
inside a body are in the focus of this conference. Dependable
communications combined with accurate localization and behavior
analysis will benefit WBAN technology and make the healthcare
processes more effective.
A brain-computer interface (BCI) is a system that interprets brain
signals generated by the user, allowing specific commands from the
brain to be sent to an external device. Such interface enables
severely disabled people to interact with their environment without
the need for any activation of their normal pathways involved in
motor commands. The combination of rehabilitation paradigms and
BCIs, both of which exploit cortical plasticity, could help people
become "able" once again. For this reason, BCI systems appear
promising rehabilitation tools. The aim of this PhD thesis is to
study how a BCI system can be used for stroke rehabilitation when
it is based on neuromodulation techniques using Hebbian plasticity
and movement related cortical potentials (MRCP) with an optimum
number of EEG electrodes. Four studies were conducted to achieve
this goal: In STUDY I the novel protocol developed in
Mrachacz-Kersting et al. 2012 had showed improvement in some
relevant clinical measures used to access functionality of motor
tasks in stroke population, when applied three times in a week as a
training paradigm. These encouraging results from our first study
alongside the Mrachacz-Kersting et al. 2012 study served as the
basis for development of a self-paced BCI system for induction of
plasticity. In STUDY II (pseudo online) detector for self-paced BCI
system, based on movement intention detection from initial negative
phase of MRCP, was proposed and tested in healthy volunteers and
then in STUDY III real online selfpaced BCI system for induction of
plasticity was implemented and tested. In STUDY IV a subject
independent detector (based on STUDY II) was developed and compared
with individualized detector. The results were promising as
difference between performances of two approaches was not
significantly different.
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