Ear-EEG sensitivity modeling for neural sources and ocular artifacts

Metin, Yarici,; Thornton, Mike; Mandic, Danilo P.

doi:10.3389/fnins.2022.997377

Cited by 8 publications

(6 citation statements)

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“…Further investigation of seizure types revealed that ear-EEG reliably captures temporal lobe seizures (86% for intracranial patients and 100% for scalp patients), which account for approximately 40% of all forms of epilepsy (Semah et al 1998 ). This finding is consistent with observations previously made about the ear-EEG modality’s sensitivity to temporal lobe activity (Yarici et al 2023 ), as well as our own hypothesis that ear-EEG would be more sensitive to temporal lobe seizures given the proximity of the earbuds to the inferior and lateral temporal lobes. It is worth noting that ear-EEG also captured all 3 frontal lobe seizures (100% sensitivity), another common seizure type trailing temporal lobe seizures.…”

Section: Discussionsupporting

confidence: 94%

“…This configuration results in one intra-ear channel (ELW - ELC) and two channels that span electrodes on both ears: inter-ear canal (ERW - ELW) and inter-ear diagonal (ERW - ELC). We expected the available inter-ear channels to be useful for observing laterally differential activity with high sensitivity, while the intra-ear channel was expected to elucidate activity more local to the left temporal lobe (Yarici et al 2023 ). The inter-ear diagonal channel provides largely redundant information and has been omitted from the figures presented here.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the external ear canal and cymba conchae are anatomically close to the temporal lobe, which is commonly implicated in the onset or propagation of focal seizures. Additional evidence suggests that electrodes placed at the opening of the ear canal might be less prone to signal attenuation for neural sources in the temporal region than those distributed about the scalp (Yarici et al 2023 ). Recent reviews have enumerated the myriad of physiological signals and health phenomena that can be captured using sensors in and around the outer ear (Ne et al 2021 ; Röddiger et al 2022 ; Bleichner and Debener 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…This study also aims to characterize how the seizure detection sensitivity of ear-EEG varies with focal seizure types and anatomic boundaries. We hypothesized that the electrodes used in the ear-EEG recording modality would (1) prove most sensitive to temporal lobe seizures and would (2) yield complementary, if not superior, signal quality to the traditional scalp montage, due to the aforementioned anatomical advantages of the recording sites (Yarici et al 2023 ). We also present a supplementary analysis characterizing the effect of the EEG recording modality on the precise timing of seizure annotation onset and offset.…”

Section: Introductionmentioning

confidence: 99%

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Using a standalone ear-EEG device for focal-onset seizure detection

Joyner,

Hsu,

Martin

et al. 2024

Bioelectron Med

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Background Seizure detection is challenging outside the clinical environment due to the lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables long-term electroencephalography (EEG) recording. This is the first study we are aware of that systematically compares the seizure detection utility of in-ear EEG with that of simultaneously recorded intracranial EEG. In addition, we present a similar comparison between simultaneously recorded in-ear EEG and scalp EEG. Methods In this foundational research, we conducted a clinical feasibility study and validated the ability of the ear-EEG system to capture focal-onset seizures against 1255 hrs of simultaneous ear-EEG data along with scalp or intracranial EEG in 20 patients with refractory focal epilepsy (11 with scalp EEG, 8 with intracranial EEG, and 1 with both). Results In a blinded, independent review of the ear-EEG signals, two epileptologists were able to detect 86.4% of the seizures that were subsequently identified using the clinical gold standard EEG modalities, with a false detection rate of 0.1 per day, well below what has been reported for ambulatory monitoring. The few seizures not detected on the ear-EEG signals emanated from deep within the mesial temporal lobe or extra-temporally and remained very focal, without significant propagation. Following multiple sessions of recording for a median continuous wear time of 13 hrs, patients reported a high degree of tolerance for the device, with only minor adverse events reported by the scalp EEG cohort. Conclusions These preliminary results demonstrate the potential of using ear-EEG to enable routine collection of complementary, prolonged, and remote neurophysiological evidence, which may permit real-time detection of paroxysmal events such as seizures and epileptiform discharges. This study suggests that the ear-EEG device may assist clinicians in making an epilepsy diagnosis, assessing treatment efficacy, and optimizing medication titration.

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Section: Discussionsupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a standalone ear-EEG device for focal-onset seizure detection

Joyner,

Hsu,

Martin

et al. 2024

Bioelectron Med

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“…In the study by Fiedler et al, the authors found that auditory attention could not be decoded with significance when only in-ear electrodes were used, even in a binaural configuration [16]. This result is to be expected, since EEG recorded from electrodes with a very small spatial separation has a very low signal-to-noise ratio (SNR) [31], [32]. Instead of using in-ear electrodes exclusively, Fiedler et al proposed to reference the in-ear electrodes to a nearby location on the scalp; the FT7 location was identified as a promising candidate.…”

Section: Introductionmentioning

confidence: 99%

Robust decoding of the speech envelope from EEG recordings through deep neural networks

2022

Self Cite

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Objective. Smart hearing aids which can decode the focus of a user's attention could considerably improve comprehension levels in noisy environments. Methods for decoding auditory attention from electroencephalography (EEG) have attracted considerable interest for this reason. Recent studies suggest that the integration of deep neural networks (DNNs) into existing auditory attention decoding algorithms is highly beneficial, although it remains unclear whether these enhanced algorithms can perform robustly in different real-world scenarios. To this end, we sought to characterise the performance of DNNs at reconstructing the envelope of an attended speech stream from EEG recordings in different listening conditions. In addition, given the relatively sparse availability of EEG data, we investigate possibility of applying subject-independent algorithms to EEG recorded from unseen individuals. Approach. Both linear models and nonlinear DNNs were employed to decode the envelope of clean speech from EEG recordings, with and without subject-specific information. The mean behaviour, as well as the variability of the reconstruction, was characterised for each model. We then trained subject-specific linear models and DNNs to reconstruct the envelope of speech in clean and noisy conditions, and investigated how well they performed in different listening scenarios. We also established that these models can be used to decode auditory attention in competing-speaker scenarios. Main results. The DNNs offered a considerable advantage over their linear counterpart at reconstructing the envelope of clean speech. This advantage persisted even when subject-specific information was unavailable at the time of training. The same DNN architectures generalised to a distinct dataset, which contained EEG recorded under a variety of listening conditions. In competing-speakers and speech-in-noise conditions, the DNNs significantly outperformed the linear models. Finally, the DNNs offered a considerable improvement over the linear approach at decoding auditory attention in competing-speakers scenarios. Significance. We present the first detailed study into the extent to which DNNs can be employed for reconstructing the envelope of an attended speech stream. We conclusively demonstrate that DNNs have the ability to improve the reconstruction of the attended speech envelope. The variance of the reconstruction error is shown to be similar for both DNNs and the linear model. Overall, DNNs are demonstrated to show promise for real-world auditory attention decoding, since they perform well in multiple listening conditions and generalise to data recorded from unseen participants.

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