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  • Cross subject mental work load classification from electroencephalographic signals with automatic artifact rejection and muscle pruning

    27 October 2017

    © Springer International Publishing AG 2016. Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

  • A comparative study of the detection of direct causal influence with bivariate and multivariate measures for EEG

    27 October 2017

    © 2014 IEEE. In this paper, we perform the first comparison of a large variety of connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength interdependence between time series are compared: information theoretic measures, model- based multivariate measures in the frequency domain, and the time domain, and phase-based measures. At the same time the phase locking index is used to consider phase relationship between signals, where the phase locking value implies that the response is delayed with respect to drive at some frequency. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well.

  • EEG source analysis of data from paralysed subjects

    27 October 2017

    © 2015 SPIE. One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

  • Detection of coupling with linear and nonlinear synchronization measures for EEG

    27 October 2017

    There has been extensive research aimed at measuring synchronization to study the relationships between complex time series, such as electroencephalography (EEG). We compare six synchronization measures: the linear measures of cross-correlation, coherence and partial coherence, and three nonlinear similarity measures, namely correntropy, phase index and mutual information. We apply these measures to simulated data (unidirectionally coupled Hénon maps) to test the detection of nonlinear and nonstationary interdependence, including in the presence of noise, and to simulated EEG. No measure fails, none is the clear winner, all measures have advantages and disadvantages. 'Best measure' depends on the research aims and data. The tests selected here for EEG research recommend correntropy as the preferred measure. © 2014 IEEE.

  • Multicenter evaluation of neurofilaments in early symptom onset amyotrophic lateral sclerosis.

    28 January 2018

    OBJECTIVE: To examine neurofilament (Nf) concentrations according to symptom onset and clinical diagnostic certainty categories of amyotrophic lateral sclerosis (ALS). METHODS: We measured Nf light chain (NfL) and phosphorylated Nf heavy chain (pNfH) CSF and NfL serum levels in patients with ALS with first symptom onset ≤6 months (n = 54) or >6 months (n = 135) from sampling, and patients with other neurologic diseases, differential diagnoses of a motor neuron disease (MND mimics), and other MND variants to determine the diagnostic accuracy in patients with ALS with early symptom onset. Samples were received multicentric and analyzed by ELISA and Simoa platform and related to other clinical measures. RESULTS: NfL and pNfH in CSF and NfL in serum were increased in early and later symptomatic phase ALS (p < 0.0001). CSF and serum NfL and CSF pNfH discriminated patients with ALS with early symptom onset from those with other neurologic diseases and MND mimics with high sensitivity (94%, 88%, 98%, and 89%, 100%, 78%) and specificity (86%, 92%, 91%, and 94%, 90%, 98%) and did not vary between clinical diagnostic categories of ALS in the early symptomatic phase group. Baseline NfL and pNfH levels were not significantly different in patients with ALS with clinical progression to definite or probable ALS at follow-up. CONCLUSION: The measurement of Nf has potential to enhance diagnostic accuracy of ALS in those presenting soon after symptom onset, and is measurable across multiple centers. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that CSF and serum Nf concentrations discriminate ALS with early symptom onset from other neurologic diseases.

  • Functional strength training and movement performance therapy for upper limb recovery early poststroke-efficacy, Neural correlates, predictive markers, and cost-effectiveness: FAST-INdiCATE trial

    19 February 2018

    © 2018 Hunter, Johansen-Berg, Ward, Kennedy, Chandler, Weir, Rothwell, Wing, Grey, Barton, Leavey, Havis, Lemon, Burridge, Dymond and Pomeroy. Background: Variation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond. Objectives: To determine whether functional strength training (FST) improves upper limb recovery more than movement performance therapy (MPT). To identify: (a) neural correlates of response and (b) whether pre-intervention neural characteristics predict response. Design: Explanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke. Participants: With some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke. Interventions: Conventional physical therapy (CPT) plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was "hands-off" progressive resistive exercise cemented into functional task training. MPT was "hands-on" sensory/facilitation techniques for smooth and accurate movement. Outcomes: The primary efficacy measure was the Action Research Arm Test (ARAT). Neural measures: fractional anisotropy (FA) corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT) of motor-evoked potentials. Analysis: Covariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response. Results: 288 Participants had: mean age of 72.2 (SD 12.5) years and mean ARAT 25.5 (18.2). For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72) for FST + CPT and 7.90 (9.18) for MPT + CPT, which did not differ statistically (p = 0.298). Correlations between ARAT change scores and baseline neural values were between 0.199, p = 0.320 for MPT + CPT RMT (n = 27) and -0.147, p = 0.385 for asymmetry of corticospinal tracts FA (n = 37). Interaction effects between neural values and ARAT change between baseline and outcome were not statistically significant. Conclusions: There was no significant difference in upper limb improvement between FST and MPT. Baseline neural measures did not correlate with upper limb recovery or predict therapy response.