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.

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.

No abstract

(Your abstract must use Normal style and must fit in this box. Your abstract should be no longer than 300 words. The box will 'expand' over 2 pages as you add text/diagrams into it.)Preparation of Your Abstract Introduction:To date most studies investigating electroencephalogram (EEG) with transcranial alternating current stimulation (tACS) have been limited to comparing EEG before/after stimulation. Although methods are now available for tACS-artefact removal, to develop closed-loop stimulation protocols these algorithms need to be reformed to operate in real-time. We present a comparison of existing artefact removal procedures implemented in real-time to determine their suitability for use in closed-loop stimulations, adjusting the stimulation parameters to match ongoing EEG activity.

Motivation: 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. Methods: Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulation 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. 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.

Abstract-Maintaining low and stable electrode contact impedances is critical for obtaining high quality signals in out-ofthe-lab EEG units. Current EEG units measure the impedance of the electrode contacts by injecting an out-of-band, typically 1 kHz, current into the head. This high frequency component is easily isolated from the true EEG to avoid introducing artifacts, but does not give direct information on the contact impedance at the wanted cortical frequencies, typically at 5-30 Hz. This paper investigates two techniques for allowing simultaneous impedance measurements at 5-30 Hz for the first time. One method uses digital processing for removing the EEG artifact that continuous in-band impedance monitoring produces. The other uses a new 36 nW notch filter for removing the interference. Both are shown to allow impedance monitoring at 5-30 Hz while leaving minimal residual artifacts in the collected EEG traces.

Abstract-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 nonconventional 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.

No abstract