Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

TMS-evoked potential propagation reflects effective brain connectivity

Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.

Self-Supervised Learning for Near-Wild Cognitive Workload Estimation

Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and decontaminated and (2) carefully labeled for accurate approximation, a costly and tedious procedure. Commercial over-the-counter devices are low-cost resolutions for the real-time collection of physiological modalities. However, they produce significant artifacts when employed outside of laboratory settings, compromising machine learning accuracies. Additionally, the physiological modalities that most successfully machine-approximate cognitive workload in everyday settings are unknown. To address these challenges, a first-ever hybrid implementation of feature selection and self-supervised machine learning techniques is introduced. This model is employed on data collected outside controlled laboratory settings to (1) identify relevant physiological modalities to machine approximate six levels of cognitive-physical workloads from a seven-modality repository and (2) postulate limited labeling experiments and machine approximate mental-physical workloads using self-supervised learning techniques.

Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke.

Adapted Beamforming: A Robust and Flexible Approach for Removing Various Types of Artifacts from TMS-EEG Data

Electroencephalogram (EEG) recorded as response to transcranial magnetic stimulation (TMS) can be highly informative of cortical reactivity and connectivity. Reliable EEG interpretation requires artifact removal as the TMS-evoked EEG can contain high-amplitude artifacts. Several methods have been proposed to uncover clean neuronal EEG responses. In practice, determining which method to select for different types of artifacts is often difficult. Here, we used a unified data cleaning framework based on beamforming to improve the algorithm selection and adaptation to the recorded signals. Beamforming properties are well understood, so they can be used to yield customized methods for EEG cleaning based on prior knowledge of the artifacts and the data. The beamforming implementations also cover, but are not limited to, the popular TMS-EEG cleaning methods: independent component analysis (ICA), signal-space projection (SSP), signal-space-projection-source-informed-reconstruction method (SSP-SIR), the source-estimate-utilizing noise-discarding algorithm (SOUND), data-driven Wiener filter (DDWiener), and the multiple-source approach. In addition to these established methods, beamforming provides a flexible way to derive novel artifact suppression algorithms by considering the properties of the recorded data. With simulated and measured TMS-EEG data, we show how to adapt the beamforming-based cleaning to different data and artifact types, namely TMS-evoked muscle artifacts, ocular artifacts, TMS-related peripheral responses, and channel noise. Importantly, beamforming implementations are fast to execute: We demonstrate how the SOUND algorithm becomes orders of magnitudes faster via beamforming. Overall, the beamforming-based spatial filtering framework can greatly enhance the selection, adaptability, and speed of EEG artifact removal.

The Effect of Stimulation Intensity, Sampling Frequency, and Sample Synchronization in TMS-EEG on the TMS Pulse Artifact Amplitude and Duration

Transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) possesses diagnostic and therapeutic benefits. However, TMS provokes a large pulse artifact that momentarily obscures the cortical response, presenting a significant challenge for EEG data interpretation. We examined how stimulation intensity (SI), EEG sampling frequency (Fs) and synchronization of stimulation with EEG sampling influence the amplitude and duration of the pulse artifact. In eight healthy subjects, single-pulse TMS was administered to the primary motor cortex, due to its well-documented responsiveness to TMS. We applied two different SIs (90% and 120% of resting motor threshold, representing the commonly used subthreshold and suprathreshold levels) and Fs (conventional 5 kHz and high frequency 20 kHz) both with TMS synchronized with the EEG sampling and the conventional non-synchronized setting. Aside from removal of the DC-offset and epoching, no preprocessing was performed to the data. Using a random forest regression model, we identified that Fs had the largest impact on both the amplitude and duration of the pulse artifact, with median variable importance values of 1.444 and 1.327, respectively, followed by SI (0.964 and 1.083) and sampling synchronization (0.223 and 0.248). This indicated that Fs and SI are crucial for minimizing prediction error and thus play a pivotal role in accurately characterizing the pulse artifact. The results of this study enable focusing some of the study design parameters to minimize TMS pulse artifact, which is essential for both enhancing the reliability of clinical TMS-EEG applications and improving the overall integrity and interpretability of TMS-EEG data.

Instrumentation for TMS‐EEG Experiment: ArTGen and a Custom EEG Interface

In transcranial magnetic stimulation (TMS) and electroencephalography (EEG) experiments, two researchers typically collaborate in the lab. This study addresses the challenge a single researcher faces in managing the TMS experiment's timing while operating the TMS coil. It introduces the Arduino Trigger Generator (ArTGen) to remotely control the timing of TMS experiments using a footswitch pedal. Moreover, a bespoke printed circuit board (PCB) is designed to interface the eegoMylab amplifier with off‐the‐shelf EEG caps. The ArTGen facilitates accurate timing of the TMS stimulator's inter‐pulse intervals (IPIs) through a footswitch pedal, enhancing researchers' control over TMS‐EEG experiments. The PCB interface provides a cost‐effective tool to extend the functionality of the eegoMylab amplifier. The integration of our PCB interface has been validated in a custom TMS‐EEG setup by analyzing TMS‐evoked potentials (TEPs), global mean field power (GMFP), butterfly plots, and event‐related spectral potentials (ERSPs). The PCB reliably preserved EEG signal integrity, ensuring accurate data acquisition. Thorough channel‐wise consistency checks across components confirmed data accuracy. ArTGen's portability and footswitch feature streamline experimental control, aiding TMS‐EEG research and clinical applications. Moreover, our PCB resolves compatibility between the eegoMylab amplifier and the Waveguard EEG cap by extending the amplifier to connect to off‐the‐shelf EEG caps. The ArTGen serves as a robust remote control tool for TMS stimulators, while our PCB interface presents a solution for integrating a customized TMS‐EEG setup. This study addresses the gap in existing TMS‐EEG research by introducing innovative technological enhancements that not only augment experimental flexibility but also streamline procedural workflows.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex

Objective

We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability.

Methods

This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP).

Results

In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach.

Conclusions

Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP.

Significance

Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Reliability and Validity of Transcranial Magnetic Stimulation-Electroencephalography Biomarkers

Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience with a multitude of applications, including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease states. However, reliability (internal and external) is not alone sufficient; biomarkers also must have validity. Validity describes closeness to a true measure of the underlying neural signal or disease state. We propose that these metrics, reliability and validity, should be evaluated and optimized before any biomarker is used to inform treatment decisions. Here, we discuss these metrics with respect to causal brain connectivity biomarkers from coupling transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We discuss controversies around TMS-EEG stemming from the multiple large off-target components (noise) and relatively weak genuine brain responses (signal), as is unfortunately often the case in noninvasive human neuroscience. We review the current state of TMS-EEG recordings, which consist of a mix of reliable noise and unreliable signal. We describe methods for evaluating TMS-EEG biomarkers, including how to assess internal and external reliability across facilities, cognitive states, brain networks, and disorders and how to validate these biomarkers using invasive neural recordings or treatment response. We provide recommendations to increase reliability and validity, discuss lessons learned, and suggest future directions for the field.

Identification and verification of a 'true' TMS evoked potential in TMS-EEG

The concurrent combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) can unveil functional neural mechanisms with applications in basic and clinical research. In particular, TMS-evoked potentials (TEPs) potentially allow studying excitability and connectivity of the cortex in a causal manner that is not easily or non-invasively attainable with other neuroimaging techniques. The TEP waveform is obtained by isolating the EEG responses phase-locked to the time of TMS application. The intended component in a TEP waveform is the cortical activation by the TMS-induced electric current, free of instrumental and physiological artifact sources. This artifact-free cortical activation can be referred to as 'true' TEP. However, due to many unwanted auxiliary effects of TMS, the interpretation of 'true' TEPs has not been free of controversy. This paper reviews the most recent understandings of 'true' TEPs and their application. In the first part of the paper, TEP components are defined according to recommended methodologies. In the second part, the verification of 'true' TEP is discussed along with its sensitivity to brain-state, age, and disease. The various proposed origins of TEP components are then presented in the context of existing literature. Throughout the paper, lessons learned from the past TMS-EEG studies are highlighted to guide the identification and interpretation of 'true' TEPs in future studies.

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

Identifying novel biomarkers with TMS-EEG - Methodological possibilities and challenges

Biomarkers are essential for understanding the underlying pathologies in brain disorders and for developing effective treatments. Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an emerging neurophysiological tool that can be used for biomarker development. This method can identify biomarkers associated with the function and dynamics of the inhibitory and excitatory neurotransmitter systems and effective connectivity between brain areas. In this review, we outline the current state of the TMS-EEG biomarker field by summarizing the existing protocols and the possibilities and challenges associated with this methodology.

Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a technique for studying cortical excitability and connectivity in health and disease, allowing basic research and potential clinical applications. A major methodological issue, severely limiting the applicability of TMS-EEG, relates to the contamination of EEG signals by artifacts of biologic or non-biologic origin. To solve this problem, several methods, based on independent component analysis (ICA), principal component analysis (PCA), signal space projection (SSP), and other approaches, have been developed over the last decade. This article is divided into two parts. In the first part, we review the theoretical background of the currently available TMS-EEG artifact removal methods. In the second part, we formally introduce the mathematics underpinnings of the cleaning methods. We classify them into spatial and temporal filters based on their properties. Since the most frequently used TMS-EEG cleaning approach are spatial filter methods, we focus on them and introduce beamforming as a unified framework of the most popular spatial filtering techniques. This unifying approach enables the comparative assessment of these methods by highlighting their differences in terms of assumptions, challenges, and applicability for different types of artifacts and data. The different properties and challenges of the methods discussed are illustrated with both simulated and recorded data. This article targets non-mathematical and mathematical audiences. Accordingly, those readers interested in essential background information on these methods can focus on Section 2. Whereas theory-oriented readers may find Section 3 helpful for making informed decisions between existing methods and developing the methodology further.

Effects of transcranial magnetic stimulation on neurobiological changes in Alzheimer's disease (Review)

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline and brain neuronal loss. A pioneering field of research in AD is brain stimulation via electromagnetic fields (EMFs), which may produce clinical benefits. Noninvasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS), have been developed to treat neurological and psychiatric disorders. The purpose of the present review is to identify neurobiological changes, including inflammatory, neurodegenerative, apoptotic, neuroprotective and genetic changes, which are associated with repetitive TMS (rTMS) treatment in patients with AD. Furthermore, it aims to evaluate the effect of TMS treatment in patients with AD and to identify the associated mechanisms. The present review highlights the changes in inflammatory and apoptotic mechanisms, mitochondrial enzymatic activities, and modulation of gene expression (microRNA expression profiles) associated with rTMS or sham procedures. At the molecular level, it has been suggested that EMFs generated by TMS may affect the cell redox status and amyloidogenic processes. TMS may also modulate gene expression by acting on both transcriptional and post‑transcriptional regulatory mechanisms. TMS may increase brain cortical excitability, induce specific potentiation phenomena, and promote synaptic plasticity and recovery of impaired functions; thus, it may re‑establish cognitive performance in patients with AD.

EEG Evoked Potentials to Repetitive Transcranial Magnetic Stimulation in Normal Volunteers: Inhibitory TMS EEG Evoked Potentials

The impact of repetitive magnetic stimulation (rTMS) on cortex varies with stimulation parameters, so it would be useful to develop a biomarker to rapidly judge effects on cortical activity, including regions other than motor cortex. This study evaluated rTMS-evoked EEG potentials (TEP) after 1 Hz of motor cortex stimulation. New features are controls for baseline amplitude and comparison to control groups of sham stimulation. We delivered 200 test pulses at 0.20 Hz before and after 1500 treatment pulses at 1 Hz. Sequences comprised AAA = active stimulation with the same coil for test–treat–test phases (n = 22); PPP = realistic placebo coil stimulation for all three phases (n = 10); and APA = active coil stimulation for tests and placebo coil stimulation for treatment (n = 15). Signal processing displayed the evoked EEG waveforms, and peaks were measured by software. ANCOVA was used to measure differences in TEP peak amplitudes in post-rTMS trials while controlling for pre-rTMS TEP peak amplitude. Post hoc analysis showed reduced P60 amplitude in the active (AAA) rTMS group versus the placebo (APA) group. The N100 peak showed a treatment effect compared to the placebo groups, but no pairwise post hoc differences. N40 showed a trend toward increase. Changes were seen in widespread EEG leads, mostly ipsilaterally. TMS-evoked EEG potentials showed reduction of the P60 peak and increase of the N100 peak, both possibly reflecting increased slow inhibition after 1 Hz of rTMS. TMS-EEG may be a useful biomarker to assay brain excitability at a seizure focus and elsewhere, but individual responses are highly variable, and the difficulty of distinguishing merged peaks complicates interpretation.

TMS-EEG responses across the lifespan: Measurement, methods for characterisation and identified responses

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows probing of the neurophysiology of any neocortical brain area in vivo with millisecond accuracy. TMS-EEG is particularly unique compared with other available neurophysiological methods, as it can measure the state and dynamics of excitatory and inhibitory systems separately. Because of these capabilities, TMS-EEG responses are sensitive to the brain state, and the responses are influenced by brain maturation and ageing, making TMS-EEG a suitable method to study age-specific pathophysiology. In this review, we outline the TMS-EEG measurement procedure, the existing methods used for characterising TMS-EEG responses and the challenges associated with identifying the responses. We also summarise the findings thus far on how TMS-EEG responses change across the lifespan and the TMS-EEG features that separate typical and atypical brain maturation and ageing. Finally, we give an overview of the gaps in current knowledge to provide directions for future studies.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Multi-scale neural decoding and analysis

Objective.

Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.

Approach.

We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.

Main results.

We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.

Significance.

This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.