33,302 research outputs found
Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation
Bioelectric source analysis in the human brain from scalp
electroencephalography (EEG) signals is sensitive to the conductivity of the
different head tissues. Conductivity values are subject dependent, so
non-invasive methods for conductivity estimation are necessary to fine tune the
EEG models. To do so, the EEG forward problem solution (so-called lead field
matrix) must be computed for a large number of conductivity configurations.
Computing one lead field requires a matrix inversion which is computationally
intensive for realistic head models. Thus, the required time for computing a
large number of lead fields can become impractical. In this work, we propose to
approximate the lead field matrix for a set of conductivity configurations,
using the exact solution only for a small set of basis points in the
conductivity space. Our approach accelerates the computing time, while
controlling the approximation error. Our method is tested for brain and skull
conductivity estimation , with simulated and measured EEG data, corresponding
to evoked somato-sensory potentials. This test demonstrates that the used
approximation does not introduce any bias and runs significantly faster than if
exact lead field were to be computed.Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
However, permission to use this material for any other purposes must be
obtained from the IEEE by sending a request to [email protected]
Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
We describe our work using linear discrimination of multi-channel electroencephalography
for single-trial detection of neural signatures of visual recognition events. We demonstrate
the approach as a methodology for relating neural variability to response variability, describing
studies for response accuracy and response latency during visual target detection.
We then show how the approach can be utilized to construct a novel type of brain-computer
interface, which we term cortically-coupled computer vision. In this application, a large
database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’
improves image search over a strictly behavioral response
Propofol Induction Reduces the Capacity for Neural Information Integration: Implications for the Mechanism of Consciousness and General Anesthesia
The cognitive unbinding paradigm suggests that the synthesis of cognitive information is attenuated by general anesthesia. Here, we investigated the functional organization of brain activities in the conscious and anesthetized states, based on characteristic functional segregation and integration of electroencephalography (EEG). EEG recordings were obtained from 14 subjects undergoing induction of general anesthesia with propofol. We quantified changes in mean information integration capacity in each band of the EEG. After induction with propofol, mean information integration capacity was reduced most prominently in the gamma band of the EEG (p=0.0001). Furthermore, we demonstrate that loss of consciousness is reflected by the breakdown of the spatiotemporal organization of gamma waves. Induction of general anesthesia with propofol reduces the capacity for information integration in the brain. These data directly support the information integration theory of consciousness and the cognitive unbinding paradigm of general anesthesia
Topological inference for EEG and MEG
Neuroimaging produces data that are continuous in one or more dimensions.
This calls for an inference framework that can handle data that approximate
functions of space, for example, anatomical images, time--frequency maps and
distributed source reconstructions of electromagnetic recordings over time.
Statistical parametric mapping (SPM) is the standard framework for whole-brain
inference in neuroimaging: SPM uses random field theory to furnish -values
that are adjusted to control family-wise error or false discovery rates, when
making topological inferences over large volumes of space. Random field theory
regards data as realizations of a continuous process in one or more dimensions.
This contrasts with classical approaches like the Bonferroni correction, which
consider images as collections of discrete samples with no continuity
properties (i.e., the probabilistic behavior at one point in the image does not
depend on other points). Here, we illustrate how random field theory can be
applied to data that vary as a function of time, space or frequency. We
emphasize how topological inference of this sort is invariant to the geometry
of the manifolds on which data are sampled. This is particularly useful in
electromagnetic studies that often deal with very smooth data on scalp or
cortical meshes. This application illustrates the versatility and simplicity of
random field theory and the seminal contributions of Keith Worsley
(1951--2009), a key architect of topological inference.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS337 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
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