Biomedical subjects

Christopher J James

Publications and source records attributed to Christopher J James.

5 recordsLinked to original sources

PubMed2006

On the analysis of single versus multiple channels of electromagnetic brain signals.

OBJECTIVE: When extracting information from electromagnetic (EM) brain function through recordings such as the electroencephalogram (EEG) it is often assumed that signal processing techniques must be applied to multiple simultaneous recordings in order to obtain useful results. However, sometimes only a single channel of EEG recording is available or desirable. In this paper we objectively assess a novel methodology which exploits only a single measurement channel to extract information of interest relatively independent of channel location (relative to the source of interest). METHODS: The method relies on a combination of a matrix of delay vectors constructed from the single channel measurement, along with constrained independent component analysis, which incorporates prior information into the process. MATERIALS: Here, we use synthetically generated seizure EEG, composed of real, normal multi-channel EEG onto which is superimposed synthetic epileptic "seizure-like" activity, at different signal-to-noise (SNR) levels, through an equivalent current dipole model. RESULTS: We show that the method can extract desired information from single channels with a reasonable accuracy even at very small SNR and from channels distant from the focus of the activity. This provides a powerful technique capable of extracting multiple sources underlying single channel recordings and will be useful in situations where only single channel EM recordings of brain function are desirable, such as would be the case in wearable or implantable recording devices.

Artificial Intelligence↗

PubMed2006

On semi-blind source separation using spatial constraints with applications in EEG analysis.

Blind 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 methods.

Algorithms↗

PubMed2005

Independent component analysis for biomedical signals.

Independent component analysis (ICA) is increasing in popularity in the field of biomedical signal processing. It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Technical advances in algorithmic developments implementing ICA are reviewed along with new directions in the field. These advances are specifically summarized with applications to biomedical signals in mind. The basic assumptions that are made when applying ICA are discussed, along with their implications when applied particularly to biomedical signals. ICA as a specific embodiment of blind source separation (BSS) is also discussed, and as a consequence the criterion used for establishing independence between sources is reviewed and this leads to the introduction of ICA/BSS techniques based on time, frequency and joint time-frequency decomposition of the data. Finally, advanced implementations of ICA are illustrated as applied to neurophysiologic signals in the form of electro-magnetic brain signals data.

Biomedical Technology↗

PubMed2003

Extracting multisource brain activity from a single electromagnetic channel.

This paper develops a methodology for the extraction of multisource brain activity using only single channel recordings of electromagnetic (EM) brain signals. Measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are used to demonstrate the utility of the method on extracting multisource activity from a single channel recording. At the heart of the method is dynamical embedding (DE) where first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. The embedding matrix contains the information we require, but in a mixed form which therefore needs to be deconstructed. In particular, we demonstrate how one form of independent component analysis (ICA) performed on the embedding matrix can deconstruct the single channel recording into its underlying informative components. The components are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. The framework has been applied to single channels of both EEG and MEG recordings and is shown to isolate multiple sources of activity which includes: (i) artifactual components such as ocular, electrocardiographic and electrode artefact, (ii) seizure components in epileptic EEG recordings, and (iii) theta band, tumour related, activity in MEG recordings. The results are intuitive and meaningful in a neurophysiological setting.

Brain↗

PubMed2003

Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis.

Independent component analysis (ICA) is a technique which extracts statistically independent components from a set of measured signals. The technique enjoys numerous applications in biomedical signal analysis in the literature, especially in the analysis of electromagnetic (EM) brain signals. Standard implementations of ICA are restrictive mainly due to the square mixing assumption-for signal recordings which have large numbers of channels, the large number of resulting extracted sources makes the subsequent analysis laborious and highly subjective. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; temporally constrained ICA (cICA) can extract signals that are statistically independent, yet which are constrained to be similar to some reference signal which can incorporate such a priori information. We demonstrate this method on a synthetic dataset and on a number of artifactual waveforms identified in multichannel recordings of EEG and MEG. cICA repeatedly converges to the desired component within a few iterations and subjective analysis shows the waveforms to be of the expected morphologies and with realistic spatial distributions. This paper shows that cICA can be applied with great success to EM brain signal analysis, with an initial application in automating artifact extraction in EEG and MEG.

Algorithms↗