Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

Bleichner, Martin G.; Debener, Stefan

doi:10.3389/fnhum.2017.00163

Cited by 164 publications

(216 citation statements)

References 65 publications

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“…This revealed that the horizontal derivations FB(R) and FB(L) were the most informative for preserving sleep‐relevant information. This is similar to work presented in Bleichner, Mirkovic, and Debener (), where the significance of cEEGrid channel orientations for picking up far‐field electrical activity was discussed. In the present study, we exploited the electrode redundancy to obtain a reliable signal representation in all instances during sleep.…”

Section: Discussionsupporting

confidence: 86%

Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

Mikkelsen

Ebajemito

Bonmatí-Carrión

et al. 2018

Journal of Sleep Research

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Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low‐cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex‐printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self‐applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (“random forests”) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter‐individual variation in sleep parameters. The results demonstrate that machine‐learning‐based scoring of around‐the‐ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine‐learning‐based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine‐learning‐based scoring holds promise for large‐scale sleep studies.

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Section: Discussionsupporting

confidence: 86%

Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

Mikkelsen

Ebajemito

Bonmatí-Carrión

et al. 2018

Journal of Sleep Research

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“…Many researchers have shown that EEG recordings from around the ear can reliably decode auditory attention (Bleichner & Debener, ; Debener, Emkes, De Vos, & Bleichner, ; Mirkovic et al., ). The success of the six‐channel near‐ear decoding presented here provides promise for applying these methods to cognitively steered hearing aids.…”

Section: Discussionmentioning

confidence: 99%

Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

O’Sullivan

Lim

Lalor

2019

Eur J of Neuroscience

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Recent work using electroencephalography has applied stimulus reconstruction techniques to identify the attended speaker in a cocktail party environment. The success of these approaches has been primarily based on the ability to detect cortical tracking of the acoustic envelope at the scalp level. However, most studies have ignored the effects of visual input, which is almost always present in naturalistic scenarios. In this study, we investigated the effects of visual input on envelope‐based cocktail party decoding in two multisensory cocktail party situations: (a) Congruent AV—facing the attended speaker while ignoring another speaker represented by the audio‐only stream and (b) Incongruent AV (eavesdropping)—attending the audio‐only speaker while looking at the unattended speaker. We trained and tested decoders for each condition separately and found that we can successfully decode attention to congruent audiovisual speech and can also decode attention when listeners were eavesdropping, i.e., looking at the face of the unattended talker. In addition to this, we found alpha power to be a reliable measure of attention to the visual speech. Using parieto‐occipital alpha power, we found that we can distinguish whether subjects are attending or ignoring the speaker's face. Considering the practical applications of these methods, we demonstrate that with only six near‐ear electrodes we can successfully determine the attended speech. This work extends the current framework for decoding attention to speech to more naturalistic scenarios, and in doing so provides additional neural measures which may be incorporated to improve decoding accuracy.

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“…Therefore, ICs reflecting EKG based on prominent QRS complexes and regular eye artifacts were removed from cEEGrid-EEG data after visual inspection (see Bleichner and Debener, 2017). All EEG data was also specifically checked for ICs reflecting horizontal eye movements and relevant ICs were removed.…”

Section: Methodsmentioning

confidence: 99%

Concealed Around-the-Ear EEG Captures Cognitive Processing in a Visual Simon Task

Pacharra

Debener

Wascher

2017

Front. Hum. Neurosci.

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In theory, miniaturized systems such as the around-the-ear electrode arrays (cEEGrids) enable mobile monitoring of the electroencephalogram (EEG) in a variety of real life situations without interfering with the natural setting. However, the research benefit of such cEEGrid recordings critically depends on their validity. To investigate whether visual and motor processing are reflected in the cEEGrid-EEG, a direct comparison of EEG that was concurrently recorded with the cEEGrids and with a high-density cap setup was conducted. Thirteen participants performed a classic Simon task in which letters were presented laterally and a lateralized choice response was executed. N1, P1 and P300 event-related potential (ERP) waveforms were extracted from cEEGrid-EEG: they were found to be strongly correlated with corresponding waveforms extracted from cap-EEG but with lower signal strength and lower signal-to-noise-ratio (SNR). Event-related lateralizations (ERLs) recorded at posterior scalp sites were well reflected in middle cEEGrid pairs. Moreover, the effect size of the Simon correspondence effect on the extracted ERLs was similar between the two systems. However, lateralizations at central cap sites were less well reflected in the cEEGrid-EEG indicating a difficulty in capturing motor response preparation and execution. These results show that well-described visual and cognitive ERPs and ERLs can be measured using the cEEGrids, while motor-related cortical potentials are not well captured. This study further demonstrates the potential and possible limitations of unobtrusive cEEGrid-EEG recordings.

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Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

Cited by 164 publications

References 65 publications

Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

Concealed Around-the-Ear EEG Captures Cognitive Processing in a Visual Simon Task

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