1,720,965 research outputs found
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
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
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
Human forearm position sense after fatigue of elbow flexor muscles
No full textAfter a period of eccentric exercise of elbow flexor muscles of one arm in young, adult human subjects, muscles became fatigued and damaged. Damage indicators were a fall in force, change in resting elbow angle and delayed onset of soreness. After the exercise, subjects were asked to match the forearm angle of one arm, whose position was set by the experimenter, with their other arm. Subjects matched the position of the unsupported reference arm, when this was unexercised, with a significantly more flexed position in their exercised indicator arm. Errors were in the opposite direction when the reference arm was exercised. The size of the errors correlated with the drop in force. Less consistent errors were observed when the reference arm was supported. A similar pattern of errors was seen after concentric exercise, which does not produce muscle damage. The data suggested that subjects were using as a position cue the perceived effort required to maintain a given forearm angle against the force of gravity. The fall in force from fatigue after exercise meant more effort was required to maintain a given position. That led to matching errors between the exercised and unexercised arms. It was concluded that while a role for muscle spindles in kinaesthesia cannot be excluded, detailed information about static limb position can be derived from the effort required to support the limb against the force of gravity
Proprioceptive sensory function in Parkinson's disease and Huntington's disease: evidence from proprioception-related EEG potentials
No full textIn both Parkinson's disease and Huntington's disease, proprioceptive sensory deficits have been suggested to contribute to the motor manifestations of the disease. Here, proprioceptive sensory function was investigated in Parkinson's disease patients, Huntington's disease patients, and healthy control subjects (each group n=8), using proprioception-related evoked potentials. Proprioception-related potentials were elicited by passive index finger movements and measured with high-density EEG. Conventional median nerve somatosensory evoked potentials (mnSEPs) were recorded in the same session. Analysis included amplitude and latency measures from selected scalp electrodes and dipole source reconstruction. We found a proprioception-related N90 component of normal latency in both Parkinson's disease and Huntington's disease. The source strength of the underlying cortical generator was normal in Parkinson's disease, but marginally reduced in Huntington's disease. Using the source location of the N20-P20 component of the mnSEP as a landmark for postcentral area 3b, the N90 was localized to the precentral motor cortex. At a latency around 170-180 ms proprioception-related potentials were explained by bilateral sensory cortex activation with an altered distribution in Parkinson's disease and a reduction of ipsilateral activation in Huntington's disease. Together, the results show largely normal early proprioception-related potentials, but changes in the cortical processing of kinaesthetic signals at longer latencies in both diseases
Absence of gaze direction effects on EEG measures of sensorimotor function
No full textObjective: Gaze direction is known to modulate the activation patterns of sensorimotor areas as seen at the single cell level and in functional magnetic resonance imaging (fMRI). To determine whether such gaze direction effects can be observed in scalp-recorded electroencephalogram (EEG) measures of sensorimotor function we investigated somatosensory evoked potentials (SEPs) and steady state movement related cortical potentials (MRPs).Methods: In two separate experiments, SEPs were elicited by electrical stimulation of the median nerve (experiment 1) and steady state MRPs were induced by 2 Hz tapping paced by an auditory cue (experiment 2), while subjects directed their gaze 15° to the left or to the right.Results: Gaze direction failed to produce any appreciable differences in the waveforms of the SEPs or MRPs. In particular, there was no effect on peak amplitude, peak latency and peak scalp topography measures of SEP and MRP components, or on spatial or temporal parameters of dipole models of the underlying cortical generators. Additional frequency domain analyses did not reveal reliable gaze-related changes in induced power at electrode sites overlying somatosensory and motor areas, or in coherence between pairs of parietal, central and frontal electrodes, across a broad range of frequencies.Conclusions: EEG measures of sensorimotor function, obtained in a non-visual motor task, are insensitive to modulatory effects of gaze direction in sensorimotor areas that are observable with fMRI
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