Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals.

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson's disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125-9.375 Hz) and band 11 (B11: 15.625-17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a significantly greater forward than backward information flow between the ROIs. This simultaneous fMRI, fNIRS and EEG study has shown through independent GC analysis of the respective time series that a bi-directional effective connectivity occurs within a cortico-cortical sensorimotor network (SMC, PMC and DLPFC) during finger movement tasks.

Comparison of EEG and MEG in source localization of induced human gamma-band oscillations during visual stimulus.

High frequency gamma oscillations are indications of information processing in cortical neuronal networks. Recently, non-invasive detection of these oscillations have become one of the main research areas in magnetoencephalography (MEG) and electroencephalography (EEG) studies. The aim of this study, which is a continuation of our previous MEG study, is to compare the capability of the two modalities (EEG and MEG) in localizing the source of the induced gamma activity due to a visual stimulus, using a spatial filtering technique known as dynamic imaging of coherent sources (DICS). To do this, the brain activity was recorded using simultaneous MEG and EEG measurement and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head modeling technique, such as, the three-shell concentric spheres and an overlapping sphere (local sphere) have been used as a forward model to calculate the external electromagnetic potentials and fields recorded by the EEG and MEG, respectively. Our results from the time-frequency analysis, at the sensor level, revealed that the parieto-occipital electrodes and sensors from both modalities showed a clear and sustained gamma-band activity throughout the post-stimulus duration and that both modalities showed similar strongest gamma-band peaks. It was difficult to interpret the spatial pattern of the gamma-band oscillatory response on the scalp, at the sensor level, for both modalities. However, the source analysis result revealed that MEG3 sensor type, which measure the derivative along the longitude, showed the source more focally and close to the visual cortex (cuneus) as compared to that of the EEG.

Impact of head modeling and sensor types in localizing human gamma-band oscillations.

An effective mechanism in neuronal communication is oscillatory neuronal synchronization. The neuronal gamma-band (30-100 Hz) synchronization is associated with attention which is induced by a certain visual stimuli. Numerous studies have shown that the gamma-band activity is observed in the visual cortex. However, impact of different head modeling techniques and sensor types to localize gamma-band activity have not yet been reported. To do this, the brain activity was recorded using 306 magnetoencephalography (MEG) sensors, consisting of 102 magnetometers and 102 pairs of planar gradiometers (one measuring the derivative of the magnetic field along the latitude and the other along the longitude), and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head models with a single-shell and overlapping spheres (local sphere) have been used as a forward model for calculating the external magnetic fields generated from the gamma-band activity. For each sensor type, the subject-specific frequency range of the gamma-band activity was obtained from the spectral analysis. The identified frequency range of interest with the highest gamma-band activity is then localized using a spatial-filtering technique known as dynamic imaging of coherent sources (DICS). The source analysis for all the subjects revealed that the gradiometer sensors which measure the derivative along the longitude, showed sources close to the visual cortex (cuneus) as compared to the other gradiometer sensors which measure the derivative along the latitude. However, using the magnetometer sensors, it was not possible to localize the sources in the region of interest. When comparing the two head models, the local-sphere model helps in localizing the source more focally as compared to the single-shell head model.

Validating the effect of muscle artifact suppression in localizing focal epilepsy.

Source localization of an epileptic seizure is becoming an important diagnostic tool in pre-surgical evaluation of epileptic patients. However, for localizing the epileptogenic zone precisely, the epileptic activity needs to be isolated from other activities that are not related to the epileptic source. In this study, we aim at an investigation of the effect of muscle artifact suppression by using a low-pass filter (LPF), independent component analysis (ICA), and a combination of ICA-LPF prior to source localization in focal epilepsy. These techniques were applied on the EEG data obtained from a left-temporal lobe epileptic patient by artificially contaminating the isolated spike interval, present in the four left-temporal electrodes, with a muscle artifact. The results show that the muscle artifact was fully suppressed. Applying the dipole and current-density reconstruction (CDR) source-analysis algorithms on the filtered data, we were able to identify the location of the epileptogenic zone similar to that of the original undistorted data.

Dipole source analysis for readiness potential and field using simultaneously measured EEG and MEG signals.

Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models.

Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner.

Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.

Discrimination of Parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal.

A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.

Cortical representation of different motor rhythms during bimanual movements.

The cortical control of bimanual and unimanual movements involves complex facilitatory and inhibitory interhemispheric interactions. We analysed the part of the cortical network directly related to the motor output by corticomuscular (64 channel EEG-EMG) and cortico-cortical (EEG-EEG) coherence and delays at the frequency of a voluntarily maintained unimanual and bimanual rhythm and in the 15-30-Hz band during isometric contractions. Voluntary rhythms of each hand showed coherence with lateral cortical areas in both hemispheres and occasionally in the frontal midline region (60-80 % of the recordings and 10-30 %, respectively). They were always coherent between both hands, and this coherence was positively correlated with the interhemispheric coherence (p < 0.01). Unilateral movements were represented mainly in the contralateral cortex (60-80 vs. 10-30 % ipsilateral, p < 0.01). Ipsilateral coherence was more common in left-hand movements, paralleled by more left-right muscle coherence. Partial corticomuscular coherence most often disappeared (p < 0.05) when the contralateral cortex was the predictor, indicating a mainly indirect connection of ipsilateral/frontomesial representations with the muscle via contralateral cortex. Interhemispheric delays had a bimodal distribution (1-10 and 15-30 ms) indicating direct and subcortical routes. Corticomuscular delays (mainly 12-25 ms) indicated fast corticospinal projections and musculocortical feedback. The 15-30-Hz corticomuscular coherence during isometric contractions (60-70 % of recordings) was strictly contralaterally represented without any peripheral left-right coherence. Thus, bilateral cortical areas generate voluntary unimanual and bimanual rhythmic movements. Interhemispheric interactions as detected by EEG-EEG coherence contribute to bimanual synchronization. This is distinct from the unilateral cortical representation of the 15-30-Hz motor rhythm during isometric movements.

Oscillating central motor networks in pathological tremors and voluntary movements. What makes the difference?

Parkinsonian tremor (PD), essential tremor (ET) and voluntarily mimicked tremor represent fundamentally different motor phenomena, yet, magnetoencephalographic and imaging data suggest their origin in the same motor centers of the brain. Using EEG-EMG coherence and coherent source analysis we found a different pattern of corticomuscular delays, time courses and central representations for the basic and double tremor frequencies typical for PD suggesting a wider range defective oscillatory activity. For the basic tremor frequency similar central representations in primary sensorimotor, prefrontal/premotor and diencephalic (e.g. thalamic) areas were reproduced for all three tremors. But renormalized partial directed coherence of the spatially filtered (source) signals revealed a mainly unidirectional flow of information from the diencephalon to cortex in voluntary tremor, e.g. a thalamocortical relay, as opposed to a bidirectional subcortico-cortical flow in PD and ET promoting uncontrollable, e.g. thalamocortical, loop oscillations. Our results help to understand why pathological tremors although originating from the physiological motor network are not under voluntary control and they may contribute to the solution of the puzzle why high frequency thalamic stimulation has a selective effect on pathological tremor leaving voluntary movement performance almost unaltered.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals.

The sources of somatosensory evoked potentials (SEPs) and fields (SEFs), which is a standard paradigm, is investigated using multichannel EEG and MEG simultaneous recordings. The hypothesis that SEP & SEF sources are generated in the posterior bank of the central sulcus is tested, and analyses are compared based on EEG only, MEG only, bandpass filtered MEG, and both combined. To locate the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for averaged head models consisting of a set of connected volumes, typically representing the skull, scalp, and brain. The location of each dipole is then estimated using fixed MUSIC and current-density-reconstruction (CDR) algorithms. For both analyses, the results demonstrate that the band-pass filtered MEG can localize the sources accurately at the desired region as compared to only EEG and unfiltered MEG. For CDR analysis, it looks like MEG affects EEG during the combined analyses. The MUSIC algorithm gives better results than CDR, and when comparing the two head models, the averaged and the realistic head models showed the same result.

The central oscillatory network of essential tremor.

The responsible pathological mechanisms of essential tremor are not yet clear. In order to understand the mechanisms of the central network its sources need to be found. The cortical sources of both the basic and first "harmonic" frequency of essential tremor are addressed in this paper. The power and coherence were estimated using the multitaper method for EEG and EMG data from 6 essential tremor patients. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. Before hand this method was validated for the application of finding multiple sources for the same oscillation in the brain by using two model simulations which indicated the accuracy of the method. In all the essential tremor patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. The source for the basic frequency and the first harmonic frequency was in the region of primary sensory motor cortex, prefrontal and in the diencephalon on the contralateral side for all the patients. Thus the generation of these two oscillations involves the same cortical areas and indicates the oscillation at double the tremor frequency is a harmonic of the basic tremor frequency.

Differentiating phase shift and delay in narrow band coherent signals.

OBJECTIVE: Differentiating between a fixed activation pattern (phase shift) and conduction time (time delay) in rhythmic signals has important physiological implications but is methodologically difficult. METHODS: Delay was estimated by the maximising coherence method and phase spectra calculated between (i) a narrow band-pass filtered AR2 process and its delayed copy for different phase shifts, (ii) the surface EMGs from two antagonistic forearm muscles with reciprocal alternating activity, and (iii) EEG and EMG data from 11 recordings in five Parkinsonian tremor patients. RESULTS: Estimated delays between the versions of the AR2 process resembled the real delay and were not significantly biased by the phase-shifts. The reciprocal alternating pattern of muscle activation was shown to be a pure phase-shift without any time delay. The phase between tremor-coherent cortical electrodes and EMG showed opposite signs and differed by 3pi/4-pi between the antagonistic muscles. Bidirectional delays between contralateral cortex and EMG did not differ between the antagonists and were in keeping with fast corticospinal transmission and feedback to the cortex for both muscles. CONCLUSIONS: Phase shifts and delays reflect different mechanisms in tremor related oscillatory interactions. SIGNIFICANCE: The maximising coherence method can differentiate between them.

Imaging coherent sources of tremor related EEG activity in patients with Parkinson's disease.

The cortical sources of both the basic and first 'harmonic' frequency of Parkinsonian tremor are addressed in this paper. The power and coherence was estimated using the multitaper method for EEG and EMG data from 6 Parkinsonian patients with a classical rest tremor. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. Before hand this method was validated for the application to the EEG by showing in 3 normal subjects that rhythmic stimuli (1-5Hz) to the median nerve leads to almost identical coherent sources for the basic and first harmonic frequency in the contralateral sensorimotor cortex which is the biologically plausible result. In all the Parkinson patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. However, the source for the basic frequency was close to the frontal midline and the first harmonic frequency was in the region of premotor and sensory motor cortex on the contralateral side for all the patients. Thus the generation of these two oscillations involves different cortical areas and possibly follows different pathways to the periphery.