2,605 research outputs found
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
In this article we present the results of our research related to the study
of correlations between specific visual stimulation and the elicited brain's
electro-physiological response collected by EEG sensors from a group of
participants. We will look at how the various characteristics of visual
stimulation affect the measured electro-physiological response of the brain and
describe the optimal parameters found that elicit a steady-state visually
evoked potential (SSVEP) in certain parts of the cerebral cortex where it can
be reliably perceived by the electrode of the EEG device. After that, we
continue with a description of the advanced machine learning pipeline model
that can perform confident classification of the collected EEG data in order to
(a) reliably distinguish signal from noise (about 85% validation score) and (b)
reliably distinguish between EEG records collected from different human
participants (about 80% validation score). Finally, we demonstrate that the
proposed method works reliably even with an inexpensive (less than $100)
consumer-grade EEG sensing device and with participants who do not have
previous experience with EEG technology (EEG illiterate). All this in
combination opens up broad prospects for the development of new types of
consumer devices, [e.g.] based on virtual reality helmets or augmented reality
glasses where EEG sensor can be easily integrated. The proposed method can be
used to improve an online user experience by providing [e.g.] password-less
user identification for VR / AR applications. It can also find a more advanced
application in intensive care units where collected EEG data can be used to
classify the level of conscious awareness of patients during anesthesia or to
automatically detect hardware failures by classifying the input signal as
noise
Practical recommendations for gradient-based training of deep architectures
Learning algorithms related to artificial neural networks and in particular
for Deep Learning may seem to involve many bells and whistles, called
hyper-parameters. This chapter is meant as a practical guide with
recommendations for some of the most commonly used hyper-parameters, in
particular in the context of learning algorithms based on back-propagated
gradient and gradient-based optimization. It also discusses how to deal with
the fact that more interesting results can be obtained when allowing one to
adjust many hyper-parameters. Overall, it describes elements of the practice
used to successfully and efficiently train and debug large-scale and often deep
multi-layer neural networks. It closes with open questions about the training
difficulties observed with deeper architectures
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