On the Origin of Cardiovascular Sounds Recorded from the Ear.

Unlabelled: It has been conjectured that sounds recorded at the ear, using microphones, originate from either heart sounds propagating from the heart to the recording site, through tissue and bone, or vascular activity at the recording site, such as vasodilation. However, prior studies have not been able to verify these conjectures. The aim of this study is to gain a deeper understanding of the signals measured in the ear using so called body-coupled microphones.

Subclinical epileptiform discharges in Alzheimer's disease are associated with increased hippocampal blood flow.

Background: In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF).

Towards ASSR-based hearing assessment using natural sounds.

. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG).

Effect of Stimulus Bandwidth on the Auditory Steady-State Response in Scalp- and Ear-EEG.

Objectives: The auditory steady-state response (ASSR) enables hearing threshold estimation based on electroencephalography (EEG) recordings. The choice of stimulus type has an impact on both the detectability and the frequency specificity of the ASSR. Amplitude modulated pure tones provide the most frequency-specific ASSR, but responses to pure tones are weak.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies.

Background: Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG).

Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring.

Background: In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD.

Long-term ear-EEG monitoring of sleep - A case study during shift work.

The interest in sleep as a potential clinical biomarker is growing, but the standard method of sleep assessment, polysomnography, is expensive, time consuming, and requires a lot of expert assistance for both set-up and interpretation. To make sleep analysis more available both in research and in the clinic, there is a need for a reliable wearable device for sleep staging. In this case study, we test ear-electroencephalography.

At-home sleep monitoring using generic ear-EEG.

Introduction: A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual.

Methods: To validate the device ten healthy subjects were recruited for a 12-night sleep study.

Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography.

While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated.

Long-Term EEG Monitoring in Patients with Alzheimer's Disease Using Ear-EEG: A Feasibility Study.

Background: Previous studies have reported that epileptiform activity may be detectible in nearly half of patients with Alzheimer's disease (AD) on long-term electroencephalographic (EEG) recordings. However, such recordings can be uncomfortable, expensive, and difficult. Ear-EEG has shown promising results for long-term EEG monitoring, but it has not been used in patients with AD.

Characterization of Dry-Contact EEG Electrodes and an Empirical Comparison of Ag/AgCl and IrO Electrodes.

Dry-contact electrodes are increasingly being used for EEG recordings in both research studies and consumer products. They are more user-friendly and better suited for long-term recordings. However, dry-contact electrodes also bring challenges with respect to the stability and impedance of the electrode-skin interface.

Self-applied ear-EEG for sleep monitoring at home.

High quality sleep monitoring is done using EEG electrodes placed on the skin. This has traditionally required assistance by an expert when the equipment needed to mounted. However, this creates a limitation in how cheap and easy it can be to record sleep in the subject's own home.

Investigation of the Effect of Spatial Filtering for Detecting Auditory Steady-State Responses Recorded from Ear-EEG.

Auditory steady-state responses (ASSRs) enable hearing threshold estimation based on electrophysiological measurements and are widely used in clinical practice. Traditionally, ASSRs are recorded from a few electroencephalography (EEG) electrodes placed on the scalp. Ear-EEG is a method in which the EEG is recorded from electrodes placed within or around the ear and is thus more suitable for use in everyday life.

Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations.

Unlabelled: Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring.

Objective: Among candidate positions, those in the facial area and around the ears have the benefit of being relatively hairless, and in our view deserve extra attention.

Investigation of low dimensional feature spaces for automatic sleep staging.

Background: Automatic sleep stage classification depends crucially on the selection of a good set of descriptive features. However, the selection of a feature set with an appropriate low computational cost without compromising classification performance is still a challenge. This study attempts to represent sleep EEG patterns using a minimum number of features, without significant performance loss.

Droplet-Based Techniques for Printing of Functional Inks for Flexible Physical Sensors.

Printed electronics (PE) is an emerging technology that uses functional inks to print electrical components and circuits on variety of substrates. This technology has opened up new possibilities to fabricate flexible, bendable, and form-fitting devices at low-cost and fast speed. There are different printing technologies in use, among which droplet-based techniques are of great interest as they provide the possibility of printing computer-controlled design patterns with high resolution, and greater production flexibility.

EEGs Vary Less Between Lab and Home Locations Than They Do Between People.

Given the rapid development of light weight EEG devices which we have witnessed the past decade, it is reasonable to ask to which extent neuroscience could now be taken outside the lab. In this study, we have designed an EEG paradigm well suited for deployment "in the wild." The paradigm is tested in repeated recordings on 20 subjects, on eight different occasions (4 in the laboratory, 4 in the subject's own home).

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG.

Purpose: To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.

Methods: Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard.

Muscle Activity Detection during Sleep by Ear-EEG.

Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment.

Predicting Sleep Classification Performance without Labels.

When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording.

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs).

Personalized automatic sleep staging with single-night data: a pilot study with Kullback-Leibler divergence regularization.

Objective: Brain waves vary between people. This work aims to improve automatic sleep staging for longitudinal sleep monitoring via personalization of algorithms based on individual characteristics extracted from sleep data recorded during the first night.

Approach: As data from a single night are very small, thereby making model training difficult, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem.

Cortical Response to Fat Taste.

We sense fat by its texture and smell, but it is still unknown whether we also taste fat despite evidence of both candidate receptors and distinct fat taste sensations. One major reason fat is still not recognized as a basic taste quality is that we first need to demonstrate its underlying neural activity. To investigate such neural fat taste activation, we recorded evoked responses to commercial cow milk products with 0.