3,285 research outputs found
J.J. McNamara from C.W. Bragg, December 10, 1911
No full textLetter to J.J. McNamara from C.W. Bragg dated December 10, 1911
Richard LaHaye, James Salisbury, and C.W. Donald McIntosh
No full textRichard LaHaye, James Salisbury, and C.W. Donald McIntoshhttps://digitalmaine.com/dmr_images/4816/thumbnail.jp
Tracking epileptiform activity in the multichannel ictal EEG using spatially constrained independent component analysis
No full textBlind source separation (BSS) methods such as independent component analysis (ICA) are increasingly being used in biomedical signal processing for decomposition of multivariate time-series, such as the multichannel electroencephalogram (EEG), into a set of underlying sources, some of which may reflect clinically relevant neurophysiological activity such as epileptic seizures or spikes. Tracking and detecting signals of interest fundamentally requires at least some a priori knowledge or assumptions regarding the spatial and/or temporal characteristics of the target sources. While such prior information is conventionally used during post-processing, it seems equally sensible to incorporate any available information into the data decomposition process from the outset. This work presents an alternative approach to source tracking in multichannel EEG, which exploits prior knowledge of the spatial topographies of the scalp voltage distributions associated with the target sources. The predetermined target topographies are used in conjunction with spatially constrained ICA to extract target source waveforms which are uncontaminated by contributions from coactive and spatially correlated brain and artifact sources. These signals can then be further analyzed in terms of their morphological, spectral or statistical properties. As illustrated in the context of epileptiform EEG, this method is useful for tracking seizures
On the use of spectrally constrained ICA applied to single-channel ictal EEG recordings within a dynamical embedding framework
No full textWithin a dynamical embedding (DE) framework it is possible to extract information on multiple-sources underlying just a single channel recording of electromagnetic brain activity. Independent Component Analysis (ICA) is a technique which, when used in conjunction with DE, can identify and extract statistically independent sources underlying these single channel recordings. However, these powerful techniques still generally require subjective a posteriori analysis in order to visualise neurophysiologically meaningful components in the outputs. For this reason we introduce a variant of ICA known as constrained ICA (cICA) which allows for the extraction of one of many sources underlying the measurement signal, through the provision of a basic reference signal. This constraint can be chosen to reflect neurophysiological prior knowledge of the sources in question given the measured signal. Here we present a technique which allows for the application of spectral constraints on single channel recordings of epileptic EEG data. We show that through a combination of DE and cICA it is possible to extract meaningful information on epileptic seizures and other rhythmic activity from just a single channel of EEG. We further show that accurate extraction of the sources of interest is not critically dependent on the closeness of the measurement channel to the location of the source activity
Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure scalp topographies
No full textConventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time–frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results
The fast ICA algorithm with spatial constraints
No full textIn many blind source separation (BSS) applications, especially for biomedical signal processing, there are specific expectations regarding the spatial and temporal characteristics of some sources, but post-hoc comparisons between source estimates and anticipated outcomes can be complicated and unreliable. One alternative is to incorporate additional prior knowledge, e.g., about the spatial topography of selected source sensor projections, into the BSS approach by means of constraints. This letter describes a modified version of the FastICA algorithm for spatially constrained BSS, where the estimates of selected columns of the mixing matrix are constrained with reference to predetermined source sensor projections
Letter from Pastor James, Dodge Center, Minnesota, to C. W. Barber, North Loup, Nebraska, January 23, 1901
No full textA letter written by James of Dodge Center, Minnesota, to C. W. Barber of North Loup, Nebraska, about a small pox scare in town. He also mentions a search for good men to serve as pastors
On semi-blind source separation using spatial constraints with applications in EEG Analysis
No full textBlind source separation (BSS) techniques, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing applications, including the analysis of multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. These methods estimate a set of sources from the observed data, which reflect the underlying physiological signal generating and mixing processes, noise and artifacts. In practice, BSS methods are often applied in the context of additional information and expectations regarding the spatial or temporal characteristics of some sources of interest, whose identification requires complicated post-hoc analysis or, more commonly, manual selection by human experts. An alternative would be to incorporate any available prior knowledge about the source signals or locations into a semi-blind source separation (SBSS) approach, effectively by imposing temporal or spatial constraints on the underlying source mixture model. This work is concerned with biomedical applications of SBSS using spatial constraints, particularly for artifact removal and source tracking in EEG analysis, and provides definitions of different types of spatial constraint along with general guidelines on how these can be implemented in conjunction with conventional BSS method
James Dunning to D.W. Siler, October 7, 1861
No full textIn a letter of October 7, 1861, James Dunning writes to D.W. Siler in reply to the latter's inquiry about the availablity of hides.James Dunning to D.W. Siler, October 7, 1861
Charleston October 7th 1861
Mr D.W. Siler,
Dear Sir,
You wrote me concern
-ing wether I had any hides and what
they were worth. I have hides sir, and
they are worth Thirteen dollars for [per ?] hun
dred dried. If you wish the wish
the hides at that price answer by
return mail and how many you
wish
Yours Respectfully
James Dunnin
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