Hearables: Artefact removal in Ear-EEG for continuous 24/7 monitoring

Occhipinti, Edoardo; Davies, Harry J.; Hammour, Ghena; Mandic, Danilo P.

doi:10.1109/ijcnn55064.2022.9892675

Cited by 14 publications

(10 citation statements)

References 17 publications

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“…This recording system therefore acquires EEG from a bilateral (cross-head) channel and a unilateral channel. Accelerometers and microphones are built into the form factors of the insert earphones: signals acquired from these sensors can be used for the purpose of denoising electrophysiological signals, as demonstrated in previous work from our research group [34], [35]. The device operates at a sampling frequency of 256 Hz.…”

Section: B Eeg Data Acquisitionmentioning

confidence: 99%

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

2022

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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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Section: B Eeg Data Acquisitionmentioning

confidence: 99%

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

2022

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“…This property also ensured that energy from abrupt motion was absorbed, leading to lower motion artifacts. The viscoelastic approach to in-ear EEG has been utilized in numerous studies, demonstrating its capabilities in acquiring in-ear EEG for various applications, however, the device design has undergone only minor adjustments since its origination [36][37][38][39][40][41][42][43][44][45][46]. Some studies directly use commercially available flexible electrode as in-ear EEG sensing device.…”

Section: Viscoelastic Earpiecementioning

confidence: 99%

“…Nonetheless, this method has only been tested with motion artifacts and may not perform as effectively with other types of noise or artifacts that could be present in real-life scenarios. The study in [45] added two microphone sensors (different positions) and an accelerometer to the viscoelastic in-ear-EEG device to capture the noises. They combined noise-assisted multivariate empirical mode decomposition (NA-MEMD) with normalized least mean square adaptive noise cancellation (NLMS-ANC) technique to produce an innovative artifact removal method for the ear-EEG data.…”

Section: Noise Cancellation and Artifact Removal Techniques On Ear-eegmentioning

confidence: 99%

The future of wearable EEG: a review of ear-EEG technology and its applications

Kaongoen,

Choi,

Woo Choi

et al. 2023

J. Neural Eng.

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Objective:This review paper provides a comprehensive overview of ear-EEG technology, which involves recording electroencephalogram (EEG) signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach:We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main Results:Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance:This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

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“…Moreover, in addition to brain monitoring, the detection of multiple other forms of vitals sign from the position of the ear have recently been established, for example the detection of heart rate, breathing rate, and blood oxygenation [36][37][38] and ECG [39]. Furthermore, the ability to provide environmental context, through classification of activities (such as talking, walking, and eating) during multi-modal ear-EEG recordings has also been demonstrated [40][41][42]; reliable separation of EEG from measurement noise, in addition to identification of the users activity, can greatly increase the utility and robustness of a wearable EEG fatigue monitoring system. Overall, 'hearable' devices hold much promise for the purpose of driver fatigue monitoring.…”

Section: Introductionmentioning

confidence: 99%

Hearables: Ear EEG Based Driver Fatigue Detection

Metin¹,

Amadori²,

Davies³

et al. 2023

Preprint

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Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical. However, the feasibility of detecting the relevant delta, theta, alpha, and beta band EEG activity through the ear EEG is yet to be investigated. Through measurements of scalp and ear EEG on ten subjects during a simulated, monotonous driving experiment, this study provides statistical analysis of characteristic ear EEG changes that are associated with the transition from alert to mentally fatigued states, and subsequent testing of a machine learning based automatic fatigue detection model. Novel numerical evidence is provided to support the feasibility of detection of mental fatigue with ear EEG that is in agreement with widely reported scalp EEG findings. This study paves the way for the development of ultra-wearable and readily deployable hearables based driver fatigue monitoring systems.

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Hearables: Artefact removal in Ear-EEG for continuous 24/7 monitoring

Cited by 14 publications

References 17 publications

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

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

The future of wearable EEG: a review of ear-EEG technology and its applications

Hearables: Ear EEG Based Driver Fatigue Detection

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