Hearables: In-Ear Multimodal Brain Computer Interfacing

Metin, Yarici,; Davies, Harry J.; Nakamura, Takashi; Williams, Ian; Mandic, Danilo P.

doi:10.1007/978-3-030-60460-8_7

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Importantly, ear worn devices are familiar, naturally discreet, unobtrusive, non-stigmatizing, and potentially easy-to-use, thus providing a convenient base for wearable health monitoring platforms (see Figure 1 ). Ear-EEG has been shown to be a reliable alternative to scalp EEG in several settings; sleep stage classification (Mikkelsen et al, 2017 ; Nakamura et al, 2017b ), drowsiness onset detection (Nakamura et al, 2018 ), objective hearing threshold estimation (Bech Christensen et al, 2018 ), bio-metric authentication (Nakamura et al, 2017a ), epileptic waveform detection (Zibrandtsen et al, 2017 ), brain-computer-interfaces (Goverdovsky et al, 2017 ; Yarici et al, 2021 ), and emotion recognition (Athavipach et al, 2019 ). Additionally, the susceptibility of ear-EEG to various artifacts has also been characterized experimentally for auditory neural activity detection in the presence of head, eye, and jaw movements (Kappel et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Ear-EEG sensitivity modeling for neural sources and ocular artifacts

2023

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The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.

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Section: Introductionmentioning

confidence: 99%

Ear-EEG sensitivity modeling for neural sources and ocular artifacts

2023

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“…The work presented by Nasri et al [16] and Veras et al [17] have discussed various methods and its associated issues towards detection of drowsiness state of driver. Yarici et al [18] have used brain-signals in order to understand the fatigued state of driver. A highly comprehensive discussion about sensors and its participation towards detecting the state of mental fatigueness is presented by Sharma et al [19].…”

Section: Introductionmentioning

confidence: 99%

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Shankara Chari,

Prashant

2024

IJ-AI

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The state of fatigueness and drowsiness relates to the stressed physical and mental condition of a driver that reduces the ability of a driver to drive safely leading to fatal consequences of road accidents. With a rising concerns about the road safety, the premium and modern vehicles are coming up with a sophisticated technology to detect and rise alarm during the positive case of fatigueness and drowsiness. Irrespective of availability of archives of literatures towards solving this problem, it is quite unclear about the strength and weakness of varied methodologies. Therefore, this paper presents a crisp and insightful discussion about the recent methodologies associated with detecting driver's attention, fatigueness, drowsiness along with highlights of commercial devices to realize various limiting factors and constraints associated with them. The paper contributes to introduce a well-structured flow of research trend to realize various patterns of current trend adopted towards solving varied problems and significant research gaps have been identified in this process. The outcome of this paper presents that still there is an open scope of an improvement towards accomplishing the agenda towards safer driving.

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“…Importantly, ear worn devices are familiar, naturally discreet, unobtrusive, non-stigmatising, and potentially easy-to-use, thus providing a convenient base for wearable health monitoring platforms. Ear-EEG has been shown to be a reliable alternative to scalp EEG in several settings; sleep stage classification [7,8], drowsiness onset detection [9], objective hearing threshold estimation [10], bio-metric authentication [11], epileptic waveform detection [12], brain-computerinterfaces [13], and emotion recognition [14]. Additionally, the susceptibility of ear-EEG to various artifacts has also been characterised experimentally for auditory neural activity detection in the presence of head, eye, and jaw movements [15].…”

Section: Introductionmentioning

confidence: 99%

Ear-EEG Sensitivity Modelling for Neural and Artifact Sources

Metin¹,

Thornton²,

Mandic³

2022

Preprint

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The ear-EEG has emerged as a promising candidate for wearable brain monitoring in real-world scenarios. While experimental studies have validated ear-EEG in multiple scenarios, the sourcesensor relationship for a variety of neural sources has not been established. In addition, a detailed theoretical analysis of the ear-EEG sensitivity to sources of artifacts is still missing. Within the present study, the sensitivity of various configurations of ear-EEG is established in the presence of neural sources from a range of brain surface locations, in addition to ocular sources for the blink, vertical saccade, and horizontal saccade eye movements which produce artifacts in the EEG signal. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG for sources that are located further away from the ears is supported through the analysis of the prominence of ocular artifacts in ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of both neural and ocular artifact sensitivity.

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Hearables: In-Ear Multimodal Brain Computer Interfacing

Cited by 3 publications

References 25 publications

Ear-EEG sensitivity modeling for neural sources and ocular artifacts

Ear-EEG sensitivity modeling for neural sources and ocular artifacts

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Ear-EEG Sensitivity Modelling for Neural and Artifact Sources

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