1,722,191 research outputs found
Lowe, D, 217806
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/400153Surname: LOWE. Given Name(s) or Initials: D. Military Service Number or Last Known Location: 217806. Missing, Wounded and Prisoner of War Enquiry Card Index Number: SEA-3736.218424
Item: [2016.0049.32446] "Lowe, D, 217806
Information dynamics view of brain processing function
No full textWe present a methodology for the analysis of electromagnetic (EM) brain signals. In a dynamical systems framework we assume that the measured electroencephalogram (EEG) and the magnetoencephalogram (MEG) are generated by the non-linear interaction of a few degrees of freedom. Within this framework, we then construct an embedding matrix, which consists of a series of consecutive delay vectors. The embedding matrix describes a trajectory on the Euclidean manifold recreating the unobservable system manifold, which is assumed to be generating the measured data. The embedding matrix can be used to quantify system complexity, which changes with the changes in brain-'state'. To this end, we use measures of entropy and Fisher's information measure to track changes in complexity of the system over time. It is also possible to perform Independent Component Analysis on the embedding matrix to decompose the single channel recording into a set of underlying independent components. The independent components are treated as a convenient expansion basis and subjective methods are used to identify components of interest relevant to the application at hand. The method is applied to just single channels of both EEG and MEG recordings and is shown to give intuitive and meaningful results in a neurophysiological setting
Using dynamical embedding to isolate seizure components in the ictal EEG
No full textA system for isolating seizure components in the epileptic electroencephalogram (EEG) is presented. The method of independent component analysis (ICA) is implemented to decompose multichannel recordings of scalp EEG known to contain epileptic seizures into their underlying independent components (ICs). The ICs are treated as a convenient expansion basis and in order to identify the relevant seizure components amongst the ICs, a series of dynamical embedding matrices are first constructed along each IC. By observing the change in structure of the singular spectra obtained by performing a singular value decomposition on each consecutive dynamical embedding matrix, it is possible to track changes in the underlying complexity of each IC with time. The change in complexity is linked to the change in entropy that can be calculated from each consecutive singular spectrum. The change in complexity, coupled with the topographical distribution of each IC, allows seizure-related components extracted by the ICA process to be subjectively identified. The method has been applied to four seizure EEG segments, and in each case probable seizure components were identified subjectively. As a proof-of-principle study, the method indicates that ICA coupled with dynamical embedding may be useful as a tool in pre-processing seizure EEG segments
Using independent component analysis and dynamical embedding to isolate seizure activity in the EEG
No full textSingle channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework
No full textThis paper introduces a method for extracting information from single channel recordings of electromagnetic (EM) brain signals. In a dynamical embedding framework, the measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are assumed generated by the non-linear interaction of a few degrees of freedom. In a three-step process, first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. Then independent component analysis (ICA) is performed on the embedding matrix to decompose the single channel recording into its underlying independent components (ICs). The ICs are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. These ICs are then projected back onto the measurement space in isolation. The method has been applied to single channels of both EEG and MEG recordings and is shown to isolate, amongst others: (i) artifactual components such as ocular, electrocardiographic and electrode artifact, (ii) seizure components in epileptic EEG recordings and (iii) theta band, tumour related, activity in MEG recordings
Isolating seizure activity in the EEG with independent component analysis artificial neural networks in medicine and biology
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