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The Oxford Handbook Of Event Related Potential Components

The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.

/pdf/wearable-eeg-and-beyond-1zks6gbilo.pdf

Wearable EEG and beyond

Objective. In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. Approach. We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. Main results. A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. Significance. Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

/pdf/mobile-eeg-on-the-bike-disentangling-attentional-and-3k8evb8pmt.pdf

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks.

Dry electrode electroencephalogram (EEG) recording combined with wireless data transmission offers an alternative tool to conventional wet electrode EEG systems. However, the question remains whether the signal quality of dry electrode recordings is comparable to wet electrode recordings in the clinical context. We recorded the resting state EEG (rsEEG), the visual evoked potentials (VEP) and the visual P300 (P3) from 16 healthy subjects (age range: 26–79 years) and 16 neurological patients who reported subjective memory impairment (age range: 50–83 years). Each subject took part in two recordings on different days, one with 19 dry electrodes and another with 19 wet electrodes. They reported their preferred EEG system. Comparisons of the rsEEG recordings were conducted qualitatively by independent visual evaluation by two neurologists blinded to the EEG system used and quantitatively by spectral analysis of the rsEEG. The P100 visual evoked potential (VEP) and P3 event-related potential (ERP) were compared in terms of latency, amplitude and pre-stimulus noise. The majority of subjects preferred the dry electrode headset. Both neurologists reported that all rsEEG traces were comparable between the wet and dry electrode headsets. Absolute Alpha and Beta power during rest did not statistically differ between the two EEG systems (p > 0.05 in all cases). However, Theta and Delta power was slightly higher with the dry electrodes (p = 0.0004 for Theta and p   0.10 in all cases) with a similar spatial distribution for both wet and dry electrode systems. These results suggest that the signal quality, ease of set-up and portability of the dry electrode EEG headset used in our study comply with the needs of clinical applications.

Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications

Transcranial alternating current stimulation (tACS) is increasingly used as a tool to non-invasively modulate brain oscillations in a frequency specific manner. A growing body of neuroscience research utilizes tACS to probe causal relationships between neuronal oscillations and cognitive processes or explore its capability of restoring dysfunctional brain oscillations implicated in various neurological and psychiatric disease. However, the underlying mechanisms of action are yet poorly understood. Due to a massive electromagnetic artifact, overlapping with the frequency of interest, direct insights to effects during stimulation from electrophysiological signals (i.e. EEG/MEG) are methodologically challenging. In the current review, we provide an overview of analysis approaches to recover brain signals in M/EEG during tACS, detailing their underlying concepts as well as limitations and methodological and interpretational pitfalls. While different analysis strategies can achieve strong attenuation of the tACS artifact in M/EEG signals, a compete removal of it is not feasible so far. However, we argue that with a combination of careful experimental designs, robust outcome measures and appropriate control analyses, valid and important insights to online effects of tACS can be revealed, enriching our understanding of its basic underlying mechanisms.

Recovering Brain Dynamics During Concurrent tACS-M/EEG: An Overview of Analysis Approaches and Their Methodological and Interpretational Pitfalls.

Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. This paper presents two different algorithms for removing the gross tACS artifact from simultaneous EEG recordings. These give different trade-offs in removal performance, in the amount of data required, and in their suitability for closed loop systems. Superposition of Moving Averages and Adaptive Filtering techniques are investigated, with significant emphasis on verification. We present head phantom testing results for controlled analysis, together with on-person EEG recordings in the time domain, frequency domain, and Event Related Potential (ERP) domain. The results show that EEG during tACS can be recovered free of large scale stimulation artifacts. Previous studies have not quantified the performance of the tACS artifact removal procedures, instead focusing on the removal of second order artifacts such as respiration related oscillations. We focus on the unresolved challenge of removing the first order stimulation artifact, presented with a new multi-stage validation strategy.

https://www.mdpi.com/1424-8220/19/1/190/pdf

Removal of Gross Artifacts of Transcranial Alternating Current Stimulation in Simultaneous EEG Monitoring.

Transcranial Current Stimulation (tCS) is a recent development for non-invasive brain stimulation. At present tCS introduces large artifacts into simultaneous EEG (electroencephalogram) recordings, which must be removed to enable brain changes from during the stimulation to be monitored. This paper presents a new approach for removing artifacts of transcranial Alternating Current Stimulation (tACS) from simultaneous EEG. Termed the Superposition of Moving Averages (SMA) our approach is independent of the number of EEG channels used and has a low computational complexity for use in real-time online artifact removal and data responsive stimulation. Compared to a Principal Component Analysis (PCA) approach our results show that SMA achieves superior artifact removal performance, especially with regards to the use of low channel count EEG devices.

/pdf/removal-of-transcranial-a-c-current-stimulation-artifact-45k1oohy1n.pdf

Removal of Transcranial a.c. Current Stimulation artifact from simultaneous EEG recordings by superposition of moving averages

Objective Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Approach Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a linear discriminant analysis was trained on the cleaned EEG to differentiate different parts of the experiment. Main results Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65%-94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process. Significance This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.

/pdf/machine-learning-validation-of-eeg-tacs-artefact-removal-4f2v2juael.pdf

Machine learning validation of EEG+tACS artefact removal

Conventional EEG (electroencephalography) has relied on wet electrodes which require conductive gel to help the electrodes make contact with the scalp. In recent years many dry electrode EEG systems have become available that do not require this gel. As a result they are quicker and easier to set up, with the potential to record the the EEG in situations and environments where it has not previously been possible. This paper investigates the practicality of using dry EEG in new non-conventional recording situations. In particular it uses a dry EEG recording system to monitor the EEG while a subject is riding an exercise bike. The results show that good-quality EEG, free from high-amplitude motion artefacts, can be collected in this challenging motion rich environment. In the frequency domain a peak of activity is seen over the motor cortex (C4) at 23 Hz starting five minutes after the start of the exercise task, giving initial insights into the on-going operation of the brain during exercise.

/pdf/towards-out-of-the-lab-eeg-in-uncontrolled-environments-4sckf6ozaf.pdf

Towards out-of-the-lab EEG in uncontrolled environments: Feasibility study of dry EEG recordings during exercise bike riding

https://www.research.manchester.ac.uk/portal/files/53067511/Abstract_BrainStim_Final.pdf

Siddharth Kohli

Papers

Removal of Gross Artifacts of Transcranial Alternating Current Stimulation in Simultaneous EEG Monitoring.

Removal of Transcranial a.c. Current Stimulation artifact from simultaneous EEG recordings by superposition of moving averages

Machine learning validation of EEG+tACS artefact removal

Towards out-of-the-lab EEG in uncontrolled environments: Feasibility study of dry EEG recordings during exercise bike riding

Towards closed-loop transcranial Electrical Stimulation: a comparison of methods for real time tES-EEG artefact removal using a phantom head model