FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan‐Mathijs

doi:10.1155/2011/156869

Cited by 8,948 publications

(7,263 citation statements)

References 20 publications

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“…The stimuli were designed so that the distance from the fixation point to the middle of the stimulus did not exceed 2 angular degrees. The data from the MEG acquisition system were continuously transmitted to the on‐line analysis computer in consecutive 500‐ms segments via a real‐time buffer mechanism (Oostenveld, Fries, Maris, & Schoffelen, 2011; Sudre et al, 2011). The time courses of four characteristic spatial patterns for the pre‐defined frequency components were extracted from a subset of 96 occipital and parietal gradiometers using the spatio–spectral decomposition (SSD) algorithm (Haufe, Dähne, & Nikulin, 2014).…”

Section: Methodsmentioning

confidence: 99%

Evidence for a general performance‐monitoring system in the human brain

Zubarev

Parkkonen

2018

Human Brain Mapping

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Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action‐monitoring system has not been tested directly. Here, we use concurrent EEG‐MEG measurements and a probabilistic learning task to demonstrate that event‐related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high‐ versus low‐surprise negative feedback, and (d) erroneous versus correct brain–computer‐interface output. We further show that these patterns originate from highly‐overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.

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Section: Methodsmentioning

confidence: 99%

Evidence for a general performance‐monitoring system in the human brain

Zubarev

Parkkonen

2018

Human Brain Mapping

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“…Data was then preprocessed with Fieldtrip (Oostenveld et al 2011). The continuous time series (resting state and mental calculation task) were first separated into segments of 4 s. Then, jump, muscle, and ocular artifacts were located using all 306 sensors and the additional EOG channels.…”

Section: Meg Recordings and Preprocessingmentioning

confidence: 99%

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

López

Garcés

Cuesta

et al. 2014

AGE

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Mild cognitive impairment (MCI) is a stage between healthy aging and dementia. It is known that in this condition the connectivity patterns are altered in the resting state and during cognitive tasks, where an extra effort seems to be necessary to overcome cognitive decline. We aimed to determine the functional connectivity AGE (2014) pattern required to deal with an internally directed cognitive state (IDICS) in healthy aging and MCI. This task differs from the most commonly employed ones in neurophysiology, since inhibition from external stimuli is needed, allowing the study of this control mechanism. To this end, magnetoencephalographic (MEG) signals were acquired from 32 healthy individuals and 38 MCI patients, both in resting state and while performing a subtraction task of two levels of difficulty. Functional connectivity was assessed with phase locking value (PLV) in five frequency bands. Compared to controls, MCIs showed higher PLV values in delta, theta, and gamma bands and an opposite pattern in alpha, beta, and gamma bands in resting state. These changes were associated with poorer neuropsychological performance. During the task, this group exhibited a hypersynchronization in delta, theta, beta, and gamma bands, which was also related to a lower cognitive performance, suggesting an abnormal functioning in this group. Contrary to controls, MCIs presented a lack of synchronization in the alpha band which may denote an inhibition deficit. Additionally, the magnitude of connectivity changes rose with the task difficulty in controls but not in MCIs, in line with the compensation-related utilization of neural circuits hypothesis (CRUNCH) model.

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“…Identification of artifact-contaminated epochs was carried out within the Fieldtrip toolbox (Oostenveld et al, 2011). Event related data was bandpass filtered between 1 and 40 Hz and baseline corrected with respect to a [-250: 0] ms time window.…”

Section: Epoch-based Artifact Rejectionmentioning

confidence: 99%

“…Independent components analysis (ICA) (Comon, 1994) and second order blind identification (SOBI) (Belouchrani et al, 1997;Tang et al, 2005) as implemented in Fieldtrip (Oostenveld et al, 2011).…”

Section: Component-based Artifact Reductionmentioning

confidence: 99%

Signal-to-noise ratio of the MEG signal after preprocessing

Gonzalez-Moreno

Aurtenetxe

Lopez-Garcia

et al. 2014

Journal of Neuroscience Methods

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Signal-to-noise ratio of the MEG signal after preprocessing HIGHLIGHTSThe signal-to-noise ratio of event-related fields is used to evaluate the effectiveness of various preprocessing algorithms for magnetoencephalography data. Signal Space Separation algorithms provide approximately a 100% increase in signal to noise ratio. Epoch-based artifact rejection and decomposition methods such as independent component analysis yielded a signal to noise ratio increase of 5-10% and 35% respectively. The use of decomposition methods seems advisable. The evaluation of the signal-to-noise ratio increase can help to guide the choice of preprocessing methods. ABSTRACTBackground: Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method: The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results: Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods:No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions: The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

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FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

Cited by 8,948 publications

References 20 publications

Evidence for a general performance‐monitoring system in the human brain

Evidence for a general performance‐monitoring system in the human brain

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

Signal-to-noise ratio of the MEG signal after preprocessing

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