A method for brain monitoring based on measuring electroencephalographic (EEG) signals from electrodes placed in-the-ear (Ear-EEG) was recently proposed. The Ear-EEG recording methodology provides a non-invasive, discreet and unobtrusive... more
A method for brain monitoring based on measuring electroencephalographic (EEG) signals from electrodes placed in-the-ear (Ear-EEG) was recently proposed. The Ear-EEG recording methodology provides a non-invasive, discreet and unobtrusive way of measuring electrical brain signals and has great potential as an enabling method for brain monitoring in everyday life. This work aims at further establishing the Ear-EEG recording methodology by considering auditory evoked potentials, and by comparing Ear-EEG responses with conventional on-scalp recordings and with well established results from the literature. It is shown that both steady state and transient responses can be obtained from Ear-EEG, and that these responses have similar characteristics and quality compared to EEG obtained from conventional on-scalp recordings.
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Background Acute adult asthma is associated with hypoxemia, whereas reported CO2 levels range from hypo -to hypercapnia. A systematic literature review was performed in order to compile the current knowledge of arterial tensions of oxygen... more
Background Acute adult asthma is associated with hypoxemia, whereas reported CO2 levels range from hypo -to hypercapnia. A systematic literature review was performed in order to compile the current knowledge of arterial tensions of oxygen (PaO2) and CO2 (PaCO2) at different degrees of asthma severity. Methods A systematic literature search in PubMed, Scopus and Web of Science yielded 21 articles from 1967 to 2008 that reported adult asthma values of PaO2 and PaCO2 together with FEV1, % of predicted (FEV1%). Weighted regression models were fitted for PaO2 and PaCO2 as functions of FEV1%. For six asthma provocation studies, pre – and post-provocation values of PaO2 and PaCO2 were compared. Results PaO2 showed a linear correlation with FEV1% (R^2 = 0.72, p All provocation studies showed significant falls in PaO2 (mean fall 3.2 kPa, p Conclusions In adult asthma, PaO2 falls linearly with the reduction in FEV1%, though there is no significant change in PaCO2 until FEV1% falls below appro...
The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non- stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An assessment on the... more
The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non- stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An assessment on the qualitative performance of the EMD method in terms of the degree of signal nature preservation of individual IMF is provided. This is archived by means of the recently proposed signal characterisation method, based upon examining the signal predicability in phase space. It is shown that the first IMF always performs best in terms of signal nature preserving. Simulation results on both linear and nonlinear benchmark signals support the analysis.
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This work introduces a novel physiological sensor, which combines electrical and mechanical modalities in a co-located arrangement, to reject motion-induced artefacts. The mechanically sensitive element consists of an electret condenser... more
This work introduces a novel physiological sensor, which combines electrical and mechanical modalities in a co-located arrangement, to reject motion-induced artefacts. The mechanically sensitive element consists of an electret condenser microphone containing a light diaphragm, allowing it to detect local mechanical displacements and disregard large-scale whole body movements. The electrically sensitive element comprises a highly flexible membrane, conductive on one side and insulating on the other. It covers the sound hole of the microphone, thereby forming an isolated pocket of air between the membrane and the diaphragm. The co-located arrangement of the modalities allows the microphone to sense mechanical disturbances directly through the electrode, thus providing an accurate proxy to artefacts caused by relative motion between the skin and the electrode. This proxy is used to reject such artefacts in the electrical physiological signals, enabling enhanced recording quality in wea...
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Seizure patterns as detected with electroencephalography (EEG) has been suggested to be frequent in patients with Alzheimer’s disease. However, long‐term EEG monitoring to detect seizure patterns requires hospitalization for longer... more
Seizure patterns as detected with electroencephalography (EEG) has been suggested to be frequent in patients with Alzheimer’s disease. However, long‐term EEG monitoring to detect seizure patterns requires hospitalization for longer periods of time, which is both expensive and difficult for patients with Alzheimer’s disease.In the current feasibility study, we investigated whether it was possible for patients with Alzheimer’s disease to wear ear‐EEG for long‐term EEG monitoring of seizure patterns at home.
This paper presents an analysis of the merits of the original Yarbus experiment on eye movements with respect to judgments on differences in cognitive layer processes. The principles thus derived are applied to the development of an... more
This paper presents an analysis of the merits of the original Yarbus experiment on eye movements with respect to judgments on differences in cognitive layer processes. The principles thus derived are applied to the development of an equivalent auditory experiment where, instead of eye movements, the response of the subject is observed by EEG measurements. Results from a preliminary trial are also included in which EEG analysis is used to ascertain the attended sound source in a multiple sound source environment. The investigation is part of ongoing research to improve the usefulness of hearing instruments and is also relevant in relation to other scientific investigations concerning the processing of sounds in complex acoustical environments by the human brain.
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Research Interests: Algorithms, Biomedical Engineering, Data Analysis, Cognition, Nonlinear dynamics, and 15 moreElectroencephalography, Brain Computer Interface, Imagination, Brain, Humans, Computer Simulation, Brain Computer Interfaces, Empirical mode decomposition, Feature Extraction, Rotation, Signal to Noise Ratio, Electrical And Electronic Engineering, Functional Laterality, Psychomotor Performance, and neural systems
The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems... more
The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and user-centered approach to brain monitoring. The in-the-ear EEG (Ear-EEG) recording concept is tested using several standard EEG paradigms, benchmarked against standard onscalp EEG, and its feasibility proven. Such a system promises a number of advantages, including fixed electrode positions, user comfort, robustness to electromagnetic interference, feedback to the user, and ease of use. The Ear-EEG platform could also support additional biosensors, extending its reach beyond EEG to provide a powerful health-monitoring system for those applications that require long recording periods in a natural environment.
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Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the... more
Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen’s kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effec...
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The second moment-based independent component analysis scheme of Molgedey and Schuster (1994) is generalized to fractional low-order moments, relevant for linear mixtures of heavy tail stable processes. The Molgedey-Schuster algorithm... more
The second moment-based independent component analysis scheme of Molgedey and Schuster (1994) is generalized to fractional low-order moments, relevant for linear mixtures of heavy tail stable processes. The Molgedey-Schuster algorithm stands out by allowing explicitly construction of the independent components. Surprisingly, this turns out to be possible also for decorrelation based on fractional low-order moments
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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... more
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 0.1%, 4%, and 38 % fat via high-density electroencephalography (EEG) from 24 human participants. The experimental design ensured that the products would only be discriminable via their potential fat taste; all stimuli were carefully controlled for differences in viscosity, lubrication, odor, temperature, and confounding tastes (sweetness, acidity, and “off-taste”) and were delivered directly onto the tongue using a set of computer-controlled syringe pumps. Advanced topographical pattern analysis revealed different neural activation to the milk products 85–134 ms after...
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Ear-EEG enables recording of EEG in real-life environments in an unprecedented discreet and minimal obtrusive way. As ear-EEG are recorded from electrodes placed in or around the ear, the spatial coverage of the potential field on the... more
Ear-EEG enables recording of EEG in real-life environments in an unprecedented discreet and minimal obtrusive way. As ear-EEG are recorded from electrodes placed in or around the ear, the spatial coverage of the potential field on the scalp is inherently limited. Despite the limited spatial coverage, the potential field in-the-ear can still be measured in multiple points and thereby provide spatial information. We present a method to perform and visualize high-density ear-EEG recordings, and illustrate the method through recordings of auditory and visually evoked steady-state responses, for a single subject. The auditory and visually evoked responses showed distinctive differences in the response field in the ear, reflecting the very different locations of the underlying cortical sources. In conclusion, high-density ear-EEG can be used to investigate how different cortical sources maps to the ear, and provides a way to select optimal electrode positions for given brain phenomena.
A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a... more
A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps ele...
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We propose and test the keyhole hypothesis-that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The... more
We propose and test the keyhole hypothesis-that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we show that the view-represented as a linear mapping-is stable across both ...
Sleep assessment is of great importance in the diagnosis and treatment of sleep disorders. In clinical practice this is typically performed based on polysomnography recordings and manual sleep staging by experts. This procedure has the... more
Sleep assessment is of great importance in the diagnosis and treatment of sleep disorders. In clinical practice this is typically performed based on polysomnography recordings and manual sleep staging by experts. This procedure has the disadvantages that the measurements are cumbersome, may have a negative influence on the sleep, and the clinical assessment is labor intensive. Addressing the latter, there has recently been encouraging progress in the field of automatic sleep staging [1]. Furthermore, a minimally obtrusive method for recording EEG from electrodes in the ear (ear-EEG) has recently been proposed [2]. The objective of this study was to investigate the feasibility of automatic sleep stage classification based on ear-EEG. This paper presents a preliminary study based on recordings from a total of 18 subjects. Sleep scoring was performed by a clinical expert based on frontal, central and occipital region EEG, as well as EOG and EMG. 5 subjects were excluded from the study because of alpha wave contamination. In one subject the standard polysomnography was supplemented by ear-EEG. A single EEG channel sleep stage classifier was implemented using the same features and the same classifier as proposed in [1]. The performance of the single channel sleep classifier based on the scalp recordings showed an 85.7 % agreement with the manual expert scoring through 10-fold inter-subject cross validation, while the performance of the ear-EEG recordings was based on a 10-fold intra-subject cross validation and showed an 82 % agreement with the manual scoring. These results suggest that automatic sleep stage classification based on ear-EEG recordings may provide similar performance as compared to single channel scalp EEG sleep stage classification. Thereby ear-EEG may be a feasible technology for future minimal intrusive sleep stage classification.
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Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements... more
Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements of the auditory steady-state response. In cases where the objective is to measure a specific feature of an event-related potential, there will be a subject specific optimal reference configuration. This work presents a method for optimizing the reference configuration for steady-state type potentials. For given electrode positions, the method maximizes the signal-to-noise (SNR) ratio of the first harmonic of the steady-state response. This is obtained by estimating a set of weights applied to the electrode signals. The method was validated on a dataset recorded from 12 subjects. The weights were estimated from one part of the dataset, and the validation was performed on another part of the dataset. For all subjects the proposed method demonstrated a robust SNR estimate, yielding on par or better SNR compared to other well-known methods.
Research Interests: Electroencephalography, Humans, Male, Young Adult, Adult, and 3 moreEar, Electrodes, and Signal to Noise Ratio
Abstract An original experimental design is combined with a novel signal processing approach so as to provide cognitive clues in the study of auditory scene analysis and in the design of auditory brain computer interfaces. Volunteers... more
Abstract An original experimental design is combined with a novel signal processing approach so as to provide cognitive clues in the study of auditory scene analysis and in the design of auditory brain computer interfaces. Volunteers attended a single auditory ...
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ABSTRACT The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non-stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An... more
ABSTRACT The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non-stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An assessment on the quali-tative ...
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The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to... more
The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the EarEEG relatively low. Recently, filter bank- and training data-based canonical correlation analysis algorithms have shown significant performance improvement in terms of accuracy of target detection and information transfer rate (ITR). This study implemented an online four-class SSVEP-based BCI system using EarEEG. Four subjects participated in offline and online BCI experiments. For the offline classification, an average accuracy of 82.71±11.83 % was obtained using 4 sec-long SSVEPs acquired from earpieces. In the online experiment, all subjects successfully completed the tasks with an average accuracy of 87.92±12.10 %, leading to an average ITR of 16.60±6.55 bits/min. The results suggest that EarEEG can be used to perform practical BCI applications. The EarEEG has the potential to be used as a portable EEG recordings platform, that could enable real-world BCI applications.
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EarEEG is a novel recordings concept where electrodes are embedded on the surface of an earpiece customized to the individual anatomical shape of the users ear. A key parameter for recording EEG signals of good quality is a stable and low... more
EarEEG is a novel recordings concept where electrodes are embedded on the surface of an earpiece customized to the individual anatomical shape of the users ear. A key parameter for recording EEG signals of good quality is a stable and low impedance electrode-body interface. This study characterizes the impedance for dry and wet EarEEG electrodes in a study of 10 subjects. A custom made and automated setup was used to characterize the impedance spectrum from 0.1 Hz-2 kHz. The study of dry electrodes showed a mean (standard deviation) low frequency impedance of the canal electrodes of 1.2 MΩ (1.4 MΩ) and the high frequency impedance was 230 kΩ (220 kΩ). For wet electrodes the low frequency impedance was 34 kΩ (37 kΩ) and the high frequency impedance was 5.1 kΩ (4.4 kΩ). The high standard deviation of the impedance for dry electrodes imposes very high requirements for the input impedance of the amplifier in order to achieve an acceptable common-mode rejection. The wet electrode impedance was in line with what is typical for a wet electrode interface.