Julio C. Hernandez-Pavon

Removal of large muscle artifacts from transcranial magnetic stimulation-evoked EEG by independent component analysis

We present two techniques utilizing independent component analysis (ICA) to remove large muscle artifacts from transcranial magnetic stimulation (TMS)-evoked EEG signals. The first one is a novel semi-automatic technique, called enhanced deflation method (EDM). EDM is a modification of the deflation mode of the FastICA algorithm; with an enhanced independent component search, EDM is an effective tool for removing the large, spiky muscle artifacts. The second technique, called manual method (MaM) makes use of the symmetric mode of FastICA and the artifactual components are visually selected by the user. In order to evaluate the success of the artifact removal methods, four different quality parameters, based on curve comparison and frequency analysis, were studied. The dorsal premotor cortex (dPMC) and Broca’s area (BA) were stimulated with TMS. Both methods removed the very large muscle artifacts recorded after stimulation of these brain areas. However, EDM was more stable, less subjective, and thus also faster to use than MaM. Until now, examining lateral areas of the cortex with TMS—EEG has been restricted because of strong muscle artifacts. The methods described here can remove those muscle artifacts, allowing one to study lateral areas of the human brain, e.g., BA, with TMS—EEG.

Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties

INTRODUCTION The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a powerful tool for causally investigating cortical mechanisms and networks. However, various artefacts contaminate TMS-EEG recordings, particularly over regions such as the dorsolateral prefrontal cortex (DLPFC). The aim of this study was to substantiate removal of artefacts from TMS-EEG recordings following stimulation of the DLPFC and motor cortex using independent component analysis (ICA). METHODS 36 healthy volunteers (30.8 ± 9 years, 9 female) received 75 single TMS pulses to the left DLPFC or left motor cortex while EEG was recorded from 57 electrodes. A subset of 9 volunteers also received 50 sham pulses. The large TMS artefact and early muscle activity (-2 to ~15 ms) were removed using interpolation and the remaining EEG signal was processed in two separate ICA runs using the FastICA algorithm. Five sub-types of TMS-related artefacts were manually identified: remaining muscle artefacts, decay artefacts, blink artefacts, auditory-evoked potentials and other noise-related artefacts. The cause of proposed blink and auditory-evoked potentials was assessed by concatenating known artefacts (i.e. voluntary blinks or auditory-evoked potentials resulting from sham TMS) to the TMS trials before ICA and evaluating grouping of resultant independent components (ICs). Finally, we assessed the effect of removing specific artefact types on TMS-evoked potentials (TEPs) and TMS-evoked oscillations. RESULTS Over DLPFC, ICs from proposed muscle and decay artefacts correlated with TMS-evoked muscle activity size, whereas proposed TMS-evoked blink ICs combined with voluntary blinks and auditory ICs with auditory-evoked potentials from sham TMS. Individual artefact sub-types characteristically distorted each measure of DLPFC function across the scalp. When free of artefact, TEPs and TMS-evoked oscillations could be measured following DLPFC stimulation. Importantly, characteristic TEPs following motor cortex stimulation (N15, P30, N45, P60, N100) could be recovered from artefactual data, corroborating the reliability of ICA-based artefact correction. CONCLUSIONS Various different artefacts contaminate TMS-EEG recordings over the DLPFC and motor cortex. However, these artefacts can be removed with apparent minimal impact on neural activity using ICA, allowing the study of TMS-evoked cortical network properties.

Effects of navigated TMS on object and action naming

Transcranial magnetic stimulation (TMS) has been used to induce speech disturbances and to affect speech performance during different naming tasks. Lately, repetitive navigated TMS (nTMS) has been used for non-invasive mapping of cortical speech-related areas. Different naming tasks may give different information that can be useful for presurgical evaluation. We studied the sensitivity of object and action naming tasks to nTMS and compared the distributions of cortical sites where nTMS produced naming errors. Eight healthy subjects named pictures of objects and actions during repetitive nTMS delivered to semi-random left-hemispheric sites. Subject-validated image stacks were obtained in the baseline naming of all pictures before nTMS. Thereafter, nTMS pulse trains were delivered while the subjects were naming the images of objects or actions. The sessions were video-recorded for offline analysis. Naming during nTMS was compared with the baseline performance. The nTMS-induced naming errors were categorized by error type and location. nTMS produced no-response errors, phonological paraphasias, and semantic paraphasias. In seven out of eight subjects, nTMS produced more errors during object than action naming. Both intrasubject and intersubject analysis showed that object naming was significantly more sensitive to nTMS. When the number of errors was compared according to a given area, nTMS to postcentral gyrus induced more errors during object than action naming. Object naming is apparently more easily disrupted by TMS than action naming. Different stimulus types can be useful for locating different aspects of speech functions. This provides new possibilities in both basic and clinical research of cortical speech representations.

Uncovering neural independent components from highly artifactual TMS-evoked EEG data

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.

Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software

ABSTRACT The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS–EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high‐frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis‐related artifacts. Finally, we introduce the TMS–EEG signal analyser (TESA), an open‐source extension for EEGLAB, which includes functions that are specific for TMS–EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS‐evoked muscle activity, and analysing TMS‐evoked potentials. The aims of TESA are to provide users with easy access to current TMS–EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open‐source functions will aid in both improving and standardising analysis across the field of TMS–EEG research. HIGHLIGHTSTMS pulses result in numerous artifacts in concurrent EEG recordings.We review the origins of these artifacts and methods for removing them.We also introduce TESA, an open‐source EEGLAB extension for TMS‐EEG analysis.

Dealing with artifacts in TMS-evoked EEG

The artifact problem in TMS-evoked EEG is analyzed in an attempt to clarify the nature of the problem and to present solutions. The best way to deal with artifacts is to avoid them; the removal or suppression of the unavoidable artifacts should be based on accurate information about their characteristics and the properties of the signal of interest.

TMS–EEG: From basic research to clinical applications

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful technique for non-invasively studying cortical excitability and connectivity. The combination of TMS and EEG has widely been used to perform basic research and recently has gained importance in different clinical applications. In this paper, we will describe the physical and biological principles of TMS–EEG and different applications in basic research and clinical applications. We will present methods based on independent component analysis (ICA) for studying the TMS-evoked EEG responses. These methods have the capability to remove and suppress large artifacts, making it feasible, for instance, to study language areas with TMS–EEG. We will discuss the different applications and limitations of TMS and TMS–EEG in clinical applications. Potential applications of TMS are presented, for instance in neurosurgical planning, depression and other neurological disorders. Advantages and disadvantages of TMS–EEG and its v...

P 220. Why do we need methods for removing artifacts from TMS-evoked EEG data?

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. In this study, TMS-EEG is used to study different brain areas. Despite the fact that there are many ways to reduce the artifacts from the EEG signals, it is still very challenging to record EEG data free from artifacts, especially when lateral areas are stimulated, e.g., language areas, or visual areas. The problems that produce the artifacts are described in detail. In this presentation I will present two approaches to deal with large artifacts. One of the approaches consists of developing methods to remove artifacts by using independent component analysis (ICA). The second approach presents methods for suppressing the artifacts rather than removing them. The methods are tested with real and simulated data. The results show that these techniques are promising in removing and suppressing artifacts, allowing one to study artifactual areas of the human brain with TMS-EEG. These methods combined with source localization open possibilities for studying functional connectivity and brain mapping of artifactual areas.

Developing of Hardware to Detect Surfaces Images on Magnetic Materials: Preliminary Results

Implementation of a scanner system for magnetic surfaces images is proposed. The Magnetometer was built with a couple of coils arranged in a first order gradiometer. The magnetic sensor was connected to an electronic circuit, then a data acquisition card, which transmitted the signal to a PC. On the other hand, the sample was placed on a xy system, for realizing the records. The conversion of the magnetic signal to pixels, was carried out with an implemented routine in LabVIEW environment. Preliminary results suggest a reasonable correlation between the original shapes and the studied surfaces.