Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies.

Conventional 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.

Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies.

Conventional 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.

Human forearm position sense after fatigue of elbow flexor muscles.

After 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.

Absence of gaze direction effects on EEG measures of sensorimotor function.

OBJECTIVE: 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 degrees 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.

Stepwise model order estimation in blind source separation applied to ictal EEG.

Most algorithms for blind source separation (BSS) or independent component analysis (ICA) assume an equal number of sources as sensors. For multichannel electrophysiological recordings, such as the electroencephalogram (EEG), however, there are often far fewer sources of neurophysiologically relevant activity than the number of sensors. This adds a model order estimation problem to the source separation problem. Conventional estimates of the number of sources are based on the dominant eigenvalues of the data covariance matrix, obtained from principal component analysis (PCA), whose corresponding eigenvectors are also used for prewhitening. It is well known that PCA is susceptible to noise, leading to incorrect model order estimates and data distortion, which in turn limit the accuracy of the source estimates. It is therefore highly desirable to determine the correct number of sources and their spatial topographies directly, without PCA-based data truncation or prewhitening. In this work, we present a stepwise BSS method for extracting only the sources necessary for a sufficiently good least-square fit to the data. This simultaneously yields model order and source estimates, which we examine at different noise levels. We also show how only a few neurophysiologically meaningful components can be extracted from 25-channel ictal EEG.

Mapping scalp topographies of rhythmic EEG activity using temporal decorrelation based constrained ICA.

Independent component analysis (ICA) methods are being increasingly applied to the analysis of electromagnetic (EM) brain signals. However, these powerful techniques still generally require subjective a posteriori analysis in order to visualise neurophysiologically meaningful components in the outputs. Standard implementations of ICA are restrictive mainly due to the square mixing assumption (i.e., as many sources as measurement channels) - this is especially so with large multichannel recordings. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; as in tracking the changing scalp topographies of rhythmic activities. Through constraining the ICA solution it is possible to extract signals that are statistically independent, yet which are similar to some reference signal which incorporates the a priori information. We demonstrate this method on a multichannel recording of an epileptiform electroencephalogram (EEG), where we automate the repeated simultaneous extraction of both rhythmic seizure activity, as well as alpha-band activity, over an epoch of EEG. Subjective analysis of the results shows scalp topographies with realistic spatial distributions which conform to our neurophysiologic expectations. This work shows that constraining ICA can be a very useful technique, especially in automated systems and we demonstrate that this can be successfully applied to EM brain signal analysis.

Neurophysiological correlates of error correction in sensorimotor-synchronization.

In a sensorimotor synchronization task requiring subjects to tap in synchrony with an auditory stimulus, occasional perturbations (i.e., interval changes) in an otherwise isochronous sequence of auditory metronome stimuli are known to be compensated remarkably swift and with surprising precision, even when they are too small to be consciously perceived. To investigate the neural substrate and the informational basis of error correction in sensorimotor synchronization, we recorded movement-related, auditory-evoked, and error-related EEG potentials. Experiment 1 confirmed rapid adjustment to stimulus phase shifts, with faster correction of large (50 ms) compared to small (15 ms) shifts. In addition to being corrected faster, there was overcorrection of the 50 ms shifts, attributed to engagement of period correction mechanisms. For +50 ms shifts, a neural correlate of period correction was identified in the form of medial frontal cortex activation, preceded by an error-related brain potential (ERN). Auditory-evoked potential (AEP) amplitudes were sensitive to stimulus phase shifts of both large and small magnitude. Further experiments with a smaller magnitude 10 ms phase shift (Experiment 2) and passive auditory stimulation (Experiment 3) provided evidence that the modulation of AEP amplitudes is not due to metronome interval changes, but may represent auditory-somatosensory activation. Together, behavioral and neurophysiological data support the hypothesis that phase correction is a largely automatic process, not dependent on conscious perception of changes in timing. By contrast, perceivable phase shifts may invoke timekeeper adjustments accompanied by medial frontal cortex activity.

Proprioceptive sensory function in Parkinson's disease and Huntington's disease: evidence from proprioception-related EEG potentials.

In 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.

Proprioception-related evoked potentials: origin and sensitivity to movement parameters.

Reafferent electroencephalography (EEG) potentials evoked by active or passive movement are largely dependent on muscle spindle input, which projects to postrolandic sensory areas as well as the precentral motor cortex. The origin of these proprioception-related evoked potentials has previously been studied by using N20-P20 source locations of the median nerve somatosensory evoked potential as an landmark for postcentral area 3b. As this approach has yielded contradictory findings, likely due to spatial undersampling, we applied dipole source analysis on two independently collected sets of high-density EEG data, containing the proprioception-related N90 elicited by passive finger movement, and the N20-P20 elicited by median nerve stimulation. In addition, the influence of movement parameters on the N90 was explored by varying amplitude/duration and direction of passive movements. The results showed that the proprioceptive N90 component was not influenced by movement direction, but had a duration that covaried with the duration of the movement. Sources were localized in the precentral cortex, located on average 10 mm anterior to the N20-P20 sources. The latter result supports earlier claims that the motor cortex is involved in the generation of proprioception-related EEG potentials.