201 research outputs found
Information Theoretic Feature Transformation Learning for Brain Interfaces
Objective: A variety of pattern analysis techniques for model training in
brain interfaces exploit neural feature dimensionality reduction based on
feature ranking and selection heuristics. In the light of broad evidence
demonstrating the potential sub-optimality of ranking based feature selection
by any criterion, we propose to extend this focus with an information theoretic
learning driven feature transformation concept. Methods: We present a maximum
mutual information linear transformation (MMI-LinT), and a nonlinear
transformation (MMI-NonLinT) framework derived by a general definition of the
feature transformation learning problem. Empirical assessments are performed
based on electroencephalographic (EEG) data recorded during a four class motor
imagery brain-computer interface (BCI) task. Exploiting state-of-the-art
methods for initial feature vector construction, we compare the proposed
approaches with conventional feature selection based dimensionality reduction
techniques which are widely used in brain interfaces. Furthermore, for the
multi-class problem, we present and exploit a hierarchical graphical model
based BCI decoding system. Results: Both binary and multi-class decoding
analyses demonstrate significantly better performances with the proposed
methods. Conclusion: Information theoretic feature transformations are capable
of tackling potential confounders of conventional approaches in various
settings. Significance: We argue that this concept provides significant
insights to extend the focus on feature selection heuristics to a broader
definition of feature transformation learning in brain interfaces.Comment: Accepted for publication by IEEE Transactions on Biomedical
Engineerin
Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
Brain extraction or whole brain segmentation is an important first step in
many of the neuroimage analysis pipelines. The accuracy and robustness of brain
extraction, therefore, is crucial for the accuracy of the entire brain analysis
process. With the aim of designing a learning-based, geometry-independent and
registration-free brain extraction tool in this study, we present a technique
based on an auto-context convolutional neural network (CNN), in which intrinsic
local and global image features are learned through 2D patches of different
window sizes. In this architecture three parallel 2D convolutional pathways for
three different directions (axial, coronal, and sagittal) implicitly learn 3D
image information without the need for computationally expensive 3D
convolutions. Posterior probability maps generated by the network are used
iteratively as context information along with the original image patches to
learn the local shape and connectedness of the brain, to extract it from
non-brain tissue.
The brain extraction results we have obtained from our algorithm are superior
to the recently reported results in the literature on two publicly available
benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap
coefficients of 97.42% and 95.40%, respectively. Furthermore, we evaluated the
performance of our algorithm in the challenging problem of extracting
arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic
resonance imaging (MRI) datasets. In this application our algorithm performed
much better than the other methods (Dice coefficient: 95.98%), where the other
methods performed poorly due to the non-standard orientation and geometry of
the fetal brain in MRI. Our CNN-based method can provide accurate,
geometry-independent brain extraction in challenging applications.Comment: This manuscripts has been submitted to TM
Fast Switch Scanning Keyboards: Minimal Expected Query Decision Trees
Augmentative and Alternative Communication (AAC) systems allow people with
disabilities to provide input to devices which empower them to more fully
interact with their environment. Within AAC, switch scanning is a common
paradigm for spelling where a set of characters is highlighted and the user is
queried as to whether their target character is in the highlighted set. These
queries are used to traverse a decision tree which successively prunes away
characters until only a single one remains (the estimate). This work seeks a
decision tree which requires the fewest expected queries per decision sequence
(EQPD). In particular, we remove the constraint that the decision tree needs to
be a row-item or group-row-item style tree and minimize EQPD. We pose the
problem as a Huffman code with variable, integer cost and solve it with a mild
extension of Golin's method in "A dynamic programming algorithm for
constructing optimal prefix-free codes with unequal letter costs", IEEE
Transactions on Information Theory (1998). Additionally, we model the user on
the query level by their probability of detection and false alarm to derive
their expected performance on the character level given some decision tree. We
perform experiments which show that the min EQPD decision tree (Karp) may
reduce selection times, especially for timed (single switch) switch scanning
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on
electroencephalographic (EEG) brain activity encounters the problem of
exploiting the temporally correlated structures or recording session specific
variability within EEG. Furthermore, recent methods have mostly trained and
evaluated based on single session EEG data. We address this problem from an
invariant representation learning perspective. We propose an adversarial
inference approach to extend such deep learning models to learn
session-invariant person-discriminative representations that can provide
robustness in terms of longitudinal usability. Using adversarial learning
within a deep convolutional network, we empirically assess and show
improvements with our approach based on longitudinally collected EEG data for
person identification from half-second EEG epochs.Comment: Accepted for publication by IEEE Signal Processing Letter
Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces
We present a novel hierarchical graphical model based context-aware hybrid
brain-machine interface (hBMI) using probabilistic fusion of
electroencephalographic (EEG) and electromyographic (EMG) activities. Based on
experimental data collected during stationary executions and subsequent
imageries of five different hand gestures with both limbs, we demonstrate
feasibility of the proposed hBMI system through within session and online
across sessions classification analyses. Furthermore, we investigate the
context-aware extent of the model by a simulated probabilistic approach and
highlight potential implications of our work in the field of
neurophysiologically-driven robotic hand prosthetics.Comment: 40th International Engineering in Medicine and Biology Conference
(EMBC 2018
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a
novel approach to transfer learning in brain-computer interfaces (BCIs). The
proposed approach aims to learn subject-invariant representations by
simultaneously training a conditional variational autoencoder (cVAE) and an
adversarial network. We use shallow convolutional architectures to realize the
cVAE, and the learned encoder is transferred to extract subject-invariant
features from unseen BCI users' data for decoding. We demonstrate a
proof-of-concept of our approach based on analyses of electroencephalographic
(EEG) data recorded during a motor imagery BCI experiment.Comment: 9th International IEEE EMBS Conference on Neural Engineering (NER'19
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
With an aim to increase the capture range and accelerate the performance of
state-of-the-art inter-subject and subject-to-template 3D registration, we
propose deep learning-based methods that are trained to find the 3D position of
arbitrarily oriented subjects or anatomy based on slices or volumes of medical
images. For this, we propose regression CNNs that learn to predict the
angle-axis representation of 3D rotations and translations using image
features. We use and compare mean square error and geodesic loss to train
regression CNNs for 3D pose estimation used in two different scenarios:
slice-to-volume registration and volume-to-volume registration. Our results
show that in such registration applications that are amendable to learning, the
proposed deep learning methods with geodesic loss minimization can achieve
accurate results with a wide capture range in real-time (<100ms). We also
tested the generalization capability of the trained CNNs on an expanded age
range and on images of newborn subjects with similar and different MR image
contrasts. We trained our models on T2-weighted fetal brain MRI scans and used
them to predict the 3D pose of newborn brains based on T1-weighted MRI scans.
We showed that the trained models generalized well for the new domain when we
performed image contrast transfer through a conditional generative adversarial
network. This indicates that the domain of application of the trained deep
regression CNNs can be further expanded to image modalities and contrasts other
than those used in training. A combination of our proposed methods with
accelerated optimization-based registration algorithms can dramatically enhance
the performance of automatic imaging devices and image processing methods of
the future.Comment: This work has been submitted to TM
Manifold unwrapping using density ridges
Research on manifold learning within a density ridge estimation framework has
shown great potential in recent work for both estimation and de-noising of
manifolds, building on the intuitive and well-defined notion of principal
curves and surfaces. However, the problem of unwrapping or unfolding manifolds
has received relatively little attention within the density ridge approach,
despite being an integral part of manifold learning in general. This paper
proposes two novel algorithms for unwrapping manifolds based on estimated
principal curves and surfaces for one- and multi-dimensional manifolds
respectively. The methods of unwrapping are founded in the realization that
both principal curves and principal surfaces will have inherent local maxima of
the probability density function. Following this observation, coordinate
systems that follow the shape of the manifold can be computed by following the
integral curves of the gradient flow of a kernel density estimate on the
manifold. Furthermore, since integral curves of the gradient flow of a kernel
density estimate is inherently local, we propose to stitch together local
coordinate systems using parallel transport along the manifold. We provide
numerical experiments on both real and synthetic data that illustrates clear
and intuitive unwrapping results comparable to state-of-the-art manifold
learning algorithms.Comment: 43 pages, 29 figures, submitted to the Journal of Machine Learning
Researc
An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling
A class of brain computer interfaces (BCIs) employs noninvasive recordings of
electroencephalography (EEG) signals to enable users with severe speech and
motor impairments to interact with their environment and social network. For
example, EEG based BCIs for typing popularly utilize event related potentials
(ERPs) for inference. Presentation paradigm design in current ERP-based letter
by letter typing BCIs typically query the user with an arbitrary subset
characters. However, the typing accuracy and also typing speed can potentially
be enhanced with more informed subset selection and flash assignment. In this
manuscript, we introduce the active recursive Bayesian state estimation
(active-RBSE) framework for inference and sequence optimization. Prior to
presentation in each iteration, rather than showing a subset of randomly
selected characters, the developed framework optimally selects a subset based
on a query function. Selected queries are made adaptively specialized for users
during each intent detection. Through a simulation-based study, we assess the
effect of active-RBSE on the performance of a language-model assisted typing
BCI in terms of typing speed and accuracy. To provide a baseline for
comparison, we also utilize standard presentation paradigms namely, row and
column matrix presentation paradigm and also random rapid serial visual
presentation paradigms. The results show that utilization of active-RBSE can
enhance the online performance of the system, both in terms of typing accuracy
and speed.Comment: 10 pages, 6 figures, Will be submitted to IEEE transactions on Signal
Processin
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry
Systems that are based on recursive Bayesian updates for classification limit
the cost of evidence collection through certain stopping/termination criteria
and accordingly enforce decision making. Conventionally, two termination
criteria based on pre-defined thresholds over (i) the maximum of the state
posterior distribution; and (ii) the state posterior uncertainty are commonly
used. In this paper, we propose a geometric interpretation over the state
posterior progression and accordingly we provide a point-by-point analysis over
the disadvantages of using such conventional termination criteria. For example,
through the proposed geometric interpretation we show that confidence
thresholds defined over maximum of the state posteriors suffer from stiffness
that results in unnecessary evidence collection whereas uncertainty based
thresholding methods are fragile to number of categories and terminate
prematurely if some state candidates are already discovered to be unfavorable.
Moreover, both types of termination methods neglect the evolution of posterior
updates. We then propose a new stopping/termination criterion with a
geometrical insight to overcome the limitations of these conventional methods
and provide a comparison in terms of decision accuracy and speed. We validate
our claims using simulations and using real experimental data obtained through
a brain computer interfaced typing system
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