193 research outputs found
Signal2Image Modules in Deep Neural Networks for EEG Classification
Deep learning has revolutionized computer vision utilizing the increased
availability of big data and the power of parallel computational units such as
graphical processing units. The vast majority of deep learning research is
conducted using images as training data, however the biomedical domain is rich
in physiological signals that are used for diagnosis and prediction problems.
It is still an open research question how to best utilize signals to train deep
neural networks.
In this paper we define the term Signal2Image (S2Is) as trainable or
non-trainable prefix modules that convert signals, such as
Electroencephalography (EEG), to image-like representations making them
suitable for training image-based deep neural networks defined as `base
models'. We compare the accuracy and time performance of four S2Is (`signal as
image', spectrogram, one and two layer Convolutional Neural Networks (CNNs))
combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet)
along with the depth-wise and 1D variations of the latter. We also provide
empirical evidence that the one layer CNN S2I performs better in eleven out of
fifteen tested models than non-trainable S2Is for classifying EEG signals and
we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201
Fast Neural Network Predictions from Constrained Aerodynamics Datasets
Incorporating computational fluid dynamics in the design process of jets,
spacecraft, or gas turbine engines is often challenged by the required
computational resources and simulation time, which depend on the chosen
physics-based computational models and grid resolutions. An ongoing problem in
the field is how to simulate these systems faster but with sufficient accuracy.
While many approaches involve simplified models of the underlying physics,
others are model-free and make predictions based only on existing simulation
data. We present a novel model-free approach in which we reformulate the
simulation problem to effectively increase the size of constrained pre-computed
datasets and introduce a novel neural network architecture (called a cluster
network) with an inductive bias well-suited to highly nonlinear computational
fluid dynamics solutions. Compared to the state-of-the-art in model-based
approximations, we show that our approach is nearly as accurate, an order of
magnitude faster, and easier to apply. Furthermore, we show that our method
outperforms other model-free approaches
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings
- …