SPEAKING PROPOSAL 2022

Unobtrusive chronic EEG acquisition with flex-printed electrode arrays
Fraunhofer IDMT
GERMANY

Brain-electrical activity as measured by the electroencephalogram (EEG) contains rich information about user states, health conditions and mental processing demands, yet this technology is largely constrained to stationary conditions. Our goal is the development of miniaturized, unobtrusive EEG systems that could be used in everyday life. Possible application scenarios are the monitoring of epileptic activity, auditory attention tracking and the assessment of sleep quality. For this purpose, we have developed different flex-printed electrode arrays (cEEGrid, fEEGrid, trEEGrid) that can be placed around the ear and along the hairline. Our devices feature good signal quality over longer periods of time without causing discomfort. Several validation studies are presented and remaining challenges for the development of everyday life EEG are identified.

Ultraflexible implantable peripheral nerve cuffs for stable, high-resolution long-term in vivo recording and stimulation
University of Cambridge
UK

Peripheral nerve implants hold great potential for the development of treatments for a wide range of clinical conditions, whether by recording electrical activity carried by nerves, or influencing it through electrical stimulation. Stable high-resolution nerve interfacing is, however, by the breakdown of the tissue-implant interface due to the development of the foreign body reaction (FBR) between the two over chronic implantation periods of time. We have developed ultraflexible parylene-C nerve cuff implants containing PEDOT:PSS multi-electrode arrays, and tested these in in vivo chronic rat forearm nerve models. Our devices minimise the development of FBR by virtue of their low-invasive cuff design, as well as the use of ultraflexible materials, while the low-impedance microelectrodes enable both sub-nerve resolution recording and simulation.

Signatures of transcranial alternating current stimulation (tACS) aftereffects in EEG
Syte Institute
Germany

Machine-learning approaches to analyze single-trial EEG signals have been mostly used in the context of brain-machine interface and in automated clinical diagnostics. In this talk I will discuss our recent study in which we classified EEG signals using machine-learning to detect subtle signatures of neuroplasticity after non-invasive transcranial alternating current stimulation (tACS). Why is this important? non-invasive brain stimulation has been out there for some time now, claiming effects on behaviour and various clinical conditions but only little evidence exist on the effects of non-invasive current stimulation on actual brain activity (and some contradicting evidence). Thus, if we want to enable this method for the next generation of theraputics for depression, migraine, tremor, and many more... we have to investigate the specific effects of this mehod on brain activity.

Prediction of Individual Intelligibility for Speech in Noise
Universitätsklinikum Erlangen - HNO-Klinik
Germany

Understanding speech in noise is one of the most challenging tasks of the auditory system. Several test methods evaluate the individual intelligibility for speech in noise. Unfortunately, all these tests depend on the cooperation of the tested person. Hence, objective methods are useful for clinical diagnostics. In our studies, we found that the EEG response pattern to sentences presented in noise systematically varies with the signal to noise ratio. These findings were used to predict whether the intelligibility of presented sentences is below or above the individual speech reception threshold with an accuracy up to 89%.

First steps to the classification of ERPs using quantum computation
IBM
FRANCE

We present a work in progress concerning the classification of EEG data using quantum computing. The practical steps of the classification pipeline are explained from an engineering point of view. Then we discuss perspectives and further research directions.

Using a Single Spectral EEG Measure Based on Differential Spectrum for Quantification of Changes in Objective Sleepiness
Federal Research Centre for Fundamental and Translational Medicine
GERMANY, RUSSIA

Accurate and objective measurement of sleepiness level can have important implications for reducing the threats of sleep loss to safety, productivity, and public health. It was previously suggested that a single EEG measure based on a differential spectrum can be used for quantification of sleepiness. The present report was aimed on providing evidence for utility of this measure for monitoring changes in objective sleepiness during sleep deprivation. I demonstrated that the time course of subjective sleepiness strongly correlated with the time cause of suggested single spectral EEG measure of sleepiness, with the correlation coefficient of higher than 0.98, and concluded that this single spectral EEG measure based can help in quantification of changes in objective sleepiness level during sleep deprivation.

Label-free detection of action potentials from membrane micromotion
University of Rostock
GERMANY

Cellular micromotion—a tiny movement of cell membranes on the nm to μm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. Imaging action potentials of single cells could be possible in this way, thanks to modern high-speed cameras and image analysis. We will present proof-of-concept work to demonstrate label-free detection of action potentials on cardiomyocyte cells. Our work harnesses several recent approaches of signal processing to detect micromotion in video recordings of unlabeled cells. Our survey includes spectral filtering of the video signal, matched filtering, as well as 1D and 3D convolutional neural networks acting on pixel-wise time-domain data and a whole recording respectively.

Continuous monitoring for early detection of neuro degenerative disease
Tata Consultancy Services
United Kingdom

Neuro-degenerative disease poses challenges and places a huge burden on the national healthcare system. Although, there is no known therapy, early detection and management can substantially decrease the burden and improve the quality of life of the patient. Continuous monitoring in real world environment can give better insights into this disease. However, capturing the EEG signals is very cumbersome. In this work, we propose Ear EEG acquisition system and early detection algorithms powered by next generation artificial intelligence. There are multiple challenges for ear-wearable EEG sensors starting from signal acquisition to electrode placement to the associated algorithms. Further to this, for the sake of validation, a low dose CT image reconstruction technique is developed that helps in confirming the progression.