Automatic detection of drowsiness using in-ear EEG

Nakamura, Takashi; Alqurashi, Yousef D.; Morrell, Mary J.; Mandic, Danilo P.

doi:10.1109/ijcnn.2018.8489723

Cited by 20 publications

(13 citation statements)

References 26 publications

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“…After this initial proof-of-concept stage, Alqurashi et al [13] conducted comprehensive multiple daytime nap recordings to establish the degree of matching of the corresponding sleep latencies based on ear-EEG and scalp-EEG under two conditions: 1) after normal sleep and 2) after sleep restriction. The same nap data over twenty three participants were used by Nakamura et al [14] to establish the potential of ear-EEG in automatic detection of drowsiness (i.e. to distinguish between wakefulness and light sleep).…”

Section: Introductionmentioning

confidence: 99%

“…Nguyen et al [15] conducted overnight sleep recordings over eight participants to evaluate their in-ear sensing system; their sensors were able to record the EEG, EOG, and EMG, key physiological variables for sleep monitoring. It is important to highlight that the sleep studies in [11][12][13][14][15], together with this study, were conducted using one-size-fits-all viscoelastic in-ear sensors, which are not optimised for a particular user but are convenient for wide deployment and promise an affordable out-of-clinic solution. Owing to their flexibility and favourable stress-strain properties (memory foam) [28], these viscoelastic earpieces can be squeezed and shaped up to fit comfortably any ear; such a 'generic' in-ear sensor is readily applicable to a large population, a pre-requisite for the future eHealth in the community.…”

Section: Introductionmentioning

confidence: 99%

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Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

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106

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Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

Self Cite

106

View full text Add to dashboard Cite

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“…Since the first research on the “in-the-ear recording concept” was published in 2012 [ 9 ], the BCI application of in-ear EEG signals has been investigated using the external stimuli such as visual or auditory cues [ 11 , 17 , 21 , 22 , 23 ] or independently of external stimuli [ 24 , 25 ]. Compared with the performance of the previous studies on the BCI application of in-ear EEG signals to mental state monitoring, our performance using the ESN technique is higher than theirs: Previous studies successfully have detected drowsiness [ 24 , 25 ], mental workload during visuomotor tracking task [ 26 ], and emotional states [ 27 ] but have required long time window (more than 10 s) to achieve high classification accuracy ( Table 5 ). In this study, we suggest that the attention monitoring system using in-ear EEG and the ESN is much faster to classify mental states than previous studies, within every 0.5 s with high accuracy of 81.16% when using one run as the test set and remaining runs as the training set within each subject.…”

Section: Discussionmentioning

confidence: 99%

“…One study has reported that drowsiness during driving simulations can be recognized from in-ear EEG signals with approximately 85% accuracy over 10 s epochs and 98.5% over 230 s epochs [ 24 ]. A similar study to measure day time drowsiness reported that in-ear EEG signals during 30 s epochs of drowsiness were discriminated from 30 s epochs of wakefulness with 80% accuracy [ 25 ]. Another study reported that mental workload and motor action during a visuomotor tracking task were detected using a two-channel in-ear EEG system with 68.55% accuracy in 5 s windows and 78.51% accuracy when a moving average filter was applied over five such windows [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

In-Ear EEG Based Attention State Classification Using Echo State Network

Jeong

2020

Brain Sciences

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It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.

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“…Generic reusable earpieces, however, are indispensable for commercializing the in-ear EEG system that all users can use immediately in real life. Several kinds of generic earpieces are developed by using memory foam with Ag-coated cloth (Goverdovsky et al, 2016, Goverdovsky et al, 2017Nakamura et al, 2018), Ag spray-coated polycarbonate (Kaveh et al, 2020), CNT/PDMS-based canaltype ear electrode cap (CEE) (Lee et al, 2014), and silvered glass silicone CEE (Dong et al, 2016). Viscoelastic flexibility and pressure between earpiece materials and ear canal allow a device to be held more softly and firmly, reducing both motion artifacts and user discomfort.…”

Section: Methods To Reduce In-ear Electroencephalography Motion Artifactsmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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Automatic detection of drowsiness using in-ear EEG

Cited by 20 publications

References 26 publications

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

In-Ear EEG Based Attention State Classification Using Echo State Network

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

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