108 research outputs found
Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study
Analogous to the mirror neuron system repeatedly described in monkeys as a
possible substrate for imitation learning and/or action understanding, a
neuronal execution/observation matching system (OEMS) is assumed in humans, but
little is known to what extent this system is activated in non-experimental,
real-life conditions. In the present case study, we investigated brain activity
of this system during natural, non-experimental motor behavior as it occurred
during playing of the board game "Malefiz". We compared spectral modulations of
the high-gamma band related to ipsilateral reaching movement execution and
observation of the same kind of movement using electrocorticography (ECoG) in
one participant. Spatially coincident activity during both conditions execution
and observation was recorded at electrode contacts over the premotor/primary
motor cortex. The topography and amplitude of the high-gamma modulations
related to both, movement observation and execution were clearly spatially
correlated over several fronto-parietal brain areas. Thus, our findings
indicate that a network of cortical areas contributes to the human OEMS, beyond
primary/premotor cortex including Brocas area and the temporo-parieto-occipital
junction area, in real-life conditions.Comment: 4 pages, 2 figure, CCN 2018 conference pape
A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications
Brain-Computer Interfaces (BCIs) comprise a rapidly evolving field of
technology with the potential of far-reaching impact in domains ranging from
medical over industrial to artistic, gaming, and military. Today, these
emerging BCI applications are typically still at early technology readiness
levels, but because BCIs create novel, technical communication channels for the
human brain, they have raised privacy and security concerns. To mitigate such
risks, a large body of countermeasures has been proposed in the literature, but
a general framework is lacking which would describe how privacy and security of
BCI applications can be protected by design, i.e., already as an integral part
of the early BCI design process, in a systematic manner, and allowing suitable
depth of analysis for different contexts such as commercial BCI product
development vs. academic research and lab prototypes. Here we propose the
adoption of recent systems-engineering methodologies for privacy threat
modeling, risk assessment, and privacy engineering to the BCI field. These
methodologies address privacy and security concerns in a more systematic and
holistic way than previous approaches, and provide reusable patterns on how to
move from principles to actions. We apply these methodologies to BCI and data
flows and derive a generic, extensible, and actionable framework for
brain-privacy-preserving cybersecurity in BCI applications. This framework is
designed for flexible application to the wide range of current and future BCI
applications. We also propose a range of novel privacy-by-design features for
BCIs, with an emphasis on features promoting BCI transparency as a prerequisite
for informational self-determination of BCI users, as well as design features
for ensuring BCI user autonomy. We anticipate that our framework will
contribute to the development of privacy-respecting, trustworthy BCI
technologies
A large-scale evaluation framework for EEG deep learning architectures
EEG is the most common signal source for noninvasive BCI applications. For
such applications, the EEG signal needs to be decoded and translated into
appropriate actions. A recently emerging EEG decoding approach is deep learning
with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
different architectures already published. Here we present a novel framework
for the large-scale evaluation of different deep-learning architectures on
different EEG datasets. This framework comprises (i) a collection of EEG
datasets currently including 100 examples (recording sessions) from six
different classification problems, (ii) a collection of different EEG decoding
algorithms, and (iii) a wrapper linking the decoders to the data as well as
handling structured documentation of all settings and (hyper-) parameters and
statistics, designed to ensure transparency and reproducibility. As an
applications example we used our framework by comparing three publicly
available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
ConvNet, and two versions of EEGNet. We also show how our framework can be used
to study similarities and differences in the performance of different decoding
methods across tasks. We argue that the deep learning EEG framework as
described here could help to tap the full potential of deep learning for BCI
applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE
SMC 2018 conferenc
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