#CCCP2018
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Lecture abstracts will be added soon
Galit Pelled
Ph.D., Professor
Director of Neroengineering division
Institute of Quantitative Health Science and Engineering
Department of Biomedical engineering, Radiology and Neuroscience
Michigan State University
East Lansing, Michigan
Aquatic inspired neuromodulation
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Tonio Ball
Translational Neurotechnology Lab
University Medical Center
Freiburg
Germany
Deep Learning for Neurotechnology
"Deep Learning (DL) based on artificial neural networks is a class of machine learning algorithms that has significantly improved the state-of-the-art in many domains, including computer vision and speech recognition tasks. DL enable the learning of hierarchies of increasingly abstract data representations, for example by training deep convolutional or recurrent neural networks (CNNs, RNNs) in an end-to end manner on the raw input signals. Today, there is a rapidly increasing interest in leveraging the ...
Mehdi Ordikhani
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Brain-computer interfaces for
augmenting attention
Guido Nolte
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Estimating brain connectivity from
EEG and MEG data: concepts, interpretations and limitations
Guenter Edlinger
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BCI with multisensory feedback
Vadim Nikulin
Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Pre-stimulus and resting state neuronal dynamics as factors defining performance in sensorimotor and cognitive tasks
" A performance in perceptual, motor and cognitive tasks depends strongly on the ongoing neuronal activity: the same stimulus leads to different brain responses and even simple motor act can’t be performed in exactly the same way. In this context, ongoing neuronal activity can be generally divided into two main classes: instantaneous and aggregate. The instantaneous ongoing activity is measured immediately before the given stimulus or response. The aggregate activity is usually measured at the resting...
Alexander Frolov
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Motor Imagery BCI in post-stroke recovery:
a clinical study
Alex Ossadtchi
Multiagent EEG Motor imagery and
EMG-based interfaces at HSE
Deep learning for learning
individual fMRI-EEG relationships
Towards zero-latency
neurofeedback
Mikhail Lebedev
Animal studies of Invasive brain-computer
interfaces
Towards Neuroadaptive Technology: An outlook on the potential impact of Passive Brain-Computer Interfaces on Technology, Neuroscience and Society
TU Berlin, Germany/ Zander Laboratories, Amsterdam, The Netherlands