![]() interactive = 'no' cfg = ft_artifact_eog ( cfg ) % detect jump artifacts in the MEG data cfg. dataset = 'SubjectCMC.ds' cfg = ft_definetrial ( cfg ) % detect EOG artifacts in the MEG data cfg. % find the interesting epochs of data cfg = cfg. Subsequently these 10-second pieces are cut into ten 1-second epochs. The epochs which correspond to a contraction of the left forearm muscle are selected. In this experiment each trigger corresponds with the start or the end of a contraction. This function uses the information provided by the triggers which were recorded simultaneously with the data. Note that this function is not part of the FieldTrip toolbox: see appendix 2, or download it here. The epochs of interest have to be defined according to a custom-written function called trialfun_left.m. Preprocessing requires the original SubjectCMC.zip MEG dataset. In this section we will apply automatic artifact rejection. We will calculate the coherence between the MEG and the EMG when the subject extended her LEFT wrist, while keeping the right forearm muscle relaxed. Subsequently it is possible to localise the neuronal sources coherent with the EMG, using ft_sourceanalysis.Visualize the results using ft_singleplotER, ft_multiplotER, and ft_topoplotER.Compute the power spectra and cross-spectral densities using the function ft_freqanalysis and subsequently compute the coherence using ft_connectivityanalysis.Read the data into MATLAB using ft_preprocessing.To compute the coherence between the MEG and EMG signals for the example dataset we will perform the following step Even though the example in this session covers cortico-muscular coherence, coherence between sensors can be calculated in exactly the same way. Secondly, we will investigate how the coherence estimate is influenced by the number of trials, and by the degree of spectral smoothing using multitaper spectral analysis. First we will explore the coherence between the EMG signal and all MEG channels. The coherence between the MEG signals and the acquired EMG will be estimated. In this session we will explore the concept of coherence by investigating a dataset from an experiment in which the subject was required to maintain an isometric contraction of a forearm muscle. The coherence values reflect the consistency of the phase difference between the two signals at a given frequency. For each frequency bin the coherence value is a number between 0 and 1. This is computed in the frequency domain by normalizing the magnitude of the summed cross-spectral density between two signals by their respective power. To study the oscillatory synchrony between two signals, one can compute the coherence. This allows us, together with the anatomical landmarks, to align source estimates of the MEG with the MRI. During the MRI scan, ear molds containing small containers filled with vitamin E marked the same landmarks. ![]() Magnetic resonance images (MRIs) were obtained from a 1.5 T Siemens system. While the subjects were seated under the MEG helmet, the positions of the coils were determined before and after the experiment by measuring the magnetic signals produced by currents passed through the coils. To measure the head position with respect to the sensors, three coils were placed at anatomical landmarks of the head (nasion, left and right ear canal). The ongoing MEG and EOG signals were lowpass filtered at 300 Hz, digitized at 1200 Hz and stored for off-line analysis. In addition, the EOG was recorded to later discard trials contaminated by eye movements and blinks. MEG signals were recorded with a 151 sensor CTF Omega System (Port Coquitlam, Canada). The bipolar EMG signal was recorded from the right extensor carpi radialis longus muscle in the lower arm. If the force was not kept constant during the course of a trial, the trial was terminated prematurely. ![]() A trial started as soon as the subject managed to get his force output within a specified range from 1 to 2 N. The subject performed two blocks of 20 trials in which either the left or the right wrist was extended for about 10 seconds. The force was monitored by strain gauges on the lever. The dataset used in this example has been recorded in an experiment in which the subject had to lift her hand and exert a constant force against a lever. In this tutorial we will analyze cortico-muscular coherence, which reflects functional connectivity between primary motor cortex and a contralateral effector muscle during isometric contraction. ![]() Tutorial coherence meg emg plotting source connectivity meg-visuomotor151 Analysis of corticomuscular coherence Introduction
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