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Sanguantrakul J, Hemakom A, Soonrach T, Israsena P. PDMS/CNT electrodes with bioamplifier for practical in-the-ear and conventional biosignal recordings. J Neural Eng 2024; 21:056017. [PMID: 39255830 DOI: 10.1088/1741-2552/ad7905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 09/10/2024] [Indexed: 09/12/2024]
Abstract
Objective.Potential usage of dry electrodes in emerging applications such as wearable devices, flexible tattoo circuits, and stretchable displays requires that, to become practical solutions, issues such as easy fabrication, strong durability, and low-cost materials must be addressed. The objective of this study was to propose soft and dry electrodes developed from polydimethylsiloxane (PDMS) and carbon nanotube (CNT) composites.Approach.The electrodes were connected with both conventional and in-house NTAmp biosignal instruments for comparative studies. The performances of the proposed dry electrodes were evaluated through electromyogram, electrocardiogram, and electroencephalogram measurements.Main results.Results demonstrated that the capability of the PDMS/CNT electrodes to receive biosignals was on par with that of commercial electrodes (adhesive and gold-cup electrodes). Depending on the type of stimuli, a signal-to-noise ratio of 5-10 dB range was achieved.Significance.The results of the study show that the performance of the proposed dry electrode is comparable to that of commercial electrodes, offering possibilities for diverse applications. These applications may include the physical examination of vital medical signs, the control of intelligent devices and robots, and the transmission of signals through flexible materials.
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Affiliation(s)
- Jongsook Sanguantrakul
- Biomedical Electronics and Systems Research Team, National Electronics and Computer Technology Center, Pathum Thani, Thailand
| | - Apit Hemakom
- Biomedical Electronics and Systems Research Team, National Electronics and Computer Technology Center, Pathum Thani, Thailand
| | - Tharapong Soonrach
- Biomedical Electronics and Systems Research Team, National Electronics and Computer Technology Center, Pathum Thani, Thailand
| | - Pasin Israsena
- Biomedical Electronics and Systems Research Team, National Electronics and Computer Technology Center, Pathum Thani, Thailand
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Kaveh R, Schwendeman C, Pu L, Arias AC, Muller R. Wireless ear EEG to monitor drowsiness. Nat Commun 2024; 15:6520. [PMID: 39095399 PMCID: PMC11297174 DOI: 10.1038/s41467-024-48682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 05/09/2024] [Indexed: 08/04/2024] Open
Abstract
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
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Affiliation(s)
- Ryan Kaveh
- University of California Berkeley, Berkeley, CA, 94708, USA.
| | | | - Leslie Pu
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Ana C Arias
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Rikky Muller
- University of California Berkeley, Berkeley, CA, 94708, USA.
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Sergeeva A, Bech Christensen C, Kidmose P. Effect of Stimulus Bandwidth on the Auditory Steady-State Response in Scalp- and Ear-EEG. Ear Hear 2024; 45:626-635. [PMID: 38178314 DOI: 10.1097/aud.0000000000001451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
OBJECTIVES The auditory steady-state response (ASSR) enables hearing threshold estimation based on electroencephalography (EEG) recordings. The choice of stimulus type has an impact on both the detectability and the frequency specificity of the ASSR. Amplitude modulated pure tones provide the most frequency-specific ASSR, but responses to pure tones are weak. The ASSR can be enhanced by increasing the bandwidth of the stimulus, but this comes at the cost of a decrease in the frequency specificity of the measured response. The objective of the present study is to investigate the relationship between stimulus bandwidth and ASSR amplitude. DESIGN The amplitude of ASSR was measured for five types of stimuli: 1 kHz pure tone and band-pass noise with 1/3, 1/2, 1, and 2 octave bandwidths centered at 1 kHz. All stimuli were amplitude modulated with a 40 Hz sinusoid. Responses to all stimulus types were measured at 30, 40, and 50 dB SL. ASSRs were measured concurrently using both conventional scalp-EEG and ear-EEG. RESULTS Stimulus bandwidth and sound intensity were both found to have a significant effect on the ASSR amplitude for scalp- and ear-EEG recordings. In scalp-EEG ASSRs to all bandwidth stimuli were found to be significantly larger than ASSRs to pure tone at low sound intensity. At higher sound intensities, however, significantly larger responses were only obtained for 1- and 2-octave bandwidth stimuli. In ear-EEG, only the ASSR to 2 octave bandwidth stimulus was significantly larger than the ASSR to amplitude modulated pure tones. CONCLUSIONS At low presentation levels, even small increases in stimulus bandwidth (1/3 and 1/2 octave) improve the detectability of ASSR in scalp-EEG with little or no impact on the frequency specificity. In comparison, a larger increase in stimulus bandwidth was needed to improve the ASSR detectability in the ear-EEG recordings.
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Affiliation(s)
- Anna Sergeeva
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
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Sergeeva A, Christensen CB, Kidmose P. Towards ASSR-based hearing assessment using natural sounds. J Neural Eng 2024; 21:026045. [PMID: 38579741 DOI: 10.1088/1741-2552/ad3b6b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 04/05/2024] [Indexed: 04/07/2024]
Abstract
Objective. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG). Ear-EEG can potentially be integrated into hearing aids, which would enable automatic fitting of the hearing device in daily life. The conventional stimuli for ASSR-based hearing assessment, such as pure tones and chirps, are monotonous and tiresome, making them inconvenient for repeated use in everyday situations. In this study we investigate the use of natural speech sounds for ASSR estimation.Approach.EEG was recorded from 22 normal hearing subjects from both scalp and ear electrodes. Subjects were stimulated monaurally with 180 min of speech stimulus modified by applying a 40 Hz amplitude modulation (AM) to an octave frequency sub-band centered at 1 kHz. Each 50 ms sub-interval in the AM sub-band was scaled to match one of 10 pre-defined levels (0-45 dB sensation level, 5 dB steps). The apparent latency for the ASSR was estimated as the maximum average cross-correlation between the envelope of the AM sub-band and the recorded EEG and was used to align the EEG signal with the audio signal. The EEG was then split up into sub-epochs of 50 ms length and sorted according to the stimulation level. ASSR was estimated for each level for both scalp- and ear-EEG.Main results. Significant ASSRs with increasing amplitude as a function of presentation level were recorded from both scalp and ear electrode configurations.Significance. Utilizing natural sounds in ASSR estimation offers the potential for electrophysiological hearing assessment that are more comfortable and less fatiguing compared to existing ASSR methods. Combined with ear-EEG, this approach may allow convenient hearing threshold estimation in everyday life, utilizing ambient sounds. Additionally, it may facilitate both initial fitting and subsequent adjustments of hearing aids outside of clinical settings.
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Affiliation(s)
- Anna Sergeeva
- Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
| | - Christian Bech Christensen
- Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
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Hammour G, Davies H, Atzori G, Della Monica C, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:448-456. [PMID: 38765887 PMCID: PMC11100860 DOI: 10.1109/jtehm.2024.3388852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
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Affiliation(s)
- Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Harry Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Giuseppe Atzori
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Ciro Della Monica
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Kiran K. G. Ravindran
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Victoria Revell
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
| | - Derk-Jan Dijk
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
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Musaeus CS, Kjaer TW, Lindberg U, Vestergaard MB, Bo H, Larsson W, Press DZ, Andersen BB, Høgh P, Kidmose P, Hemmsen MC, Rank ML, Hasselbalch SG, Waldemar G, Frederiksen KS. Subclinical epileptiform discharges in Alzheimer's disease are associated with increased hippocampal blood flow. Alzheimers Res Ther 2024; 16:80. [PMID: 38610005 PMCID: PMC11010418 DOI: 10.1186/s13195-024-01432-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/19/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF). The objective of the study was to investigate the association between rCBF in hippocampus and epileptiform discharges as measured with ear-EEG in patients with Alzheimer's disease. Our hypothesis was that increased spike frequency may be associated with increased rCBF in hippocampus. METHODS A total of 24 patients with AD, and 15 HC were included in the analysis. Using linear regression, we investigated the association between rCBF as measured with arterial spin-labelling MRI (ASL-MRI) in the hippocampus and the number of spikes/sharp waves per 24 h as assessed by ear-EEG. RESULTS No significant difference in hippocampal rCBF was found between AD and HC (p-value = 0.367). A significant linear association between spike frequency and normalized rCBF in the hippocampus was found for patients with AD (estimate: 0.109, t-value = 4.03, p-value < 0.001). Changes in areas that typically show group differences (temporal-parietal cortex) were found in patients with AD, compared to HC. CONCLUSIONS Increased spike frequency was accompanied by a hemodynamic response of increased blood flow in the hippocampus in patients with AD. This phenomenon has also been shown in patients with epilepsy and supports the hypothesis of hyperexcitability in patients with AD. The lack of a significant difference in hippocampal rCBF may be due to an increased frequency of epileptiform discharges in patients with AD. TRIAL REGISTRATION The study is registered at clinicaltrials.gov (NCT04436341).
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Affiliation(s)
- Christian Sandøe Musaeus
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark.
| | - Troels Wesenberg Kjaer
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
| | - Ulrich Lindberg
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, University of Copenhagen, Valdemar Hansens Vej 13, Glostrup, 2600, Denmark
| | - Mark B Vestergaard
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, University of Copenhagen, Valdemar Hansens Vej 13, Glostrup, 2600, Denmark
| | - Henrik Bo
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
| | - Wiberg Larsson
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, University of Copenhagen, Valdemar Hansens Vej 13, Glostrup, 2600, Denmark
| | - Daniel Zvi Press
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Birgitte Bo Andersen
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
| | - Peter Høgh
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Vestermarksvej 11, Roskilde, 4000, Denmark
- Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, Aarhus N, 8200, Denmark
| | | | | | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
- Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
- Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, Copenhagen, 2100, Denmark
- Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark
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Ladouce S, Pietzker M, Manzey D, Dehais F. Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment. Behav Brain Res 2024; 460:114827. [PMID: 38128886 DOI: 10.1016/j.bbr.2023.114827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/23/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023]
Abstract
Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.
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Affiliation(s)
- Simon Ladouce
- Human Factors and Neuroergonomics, ISAE-SUPAERO, 10 Av. Edouard Belin, Toulouse 31400, Haute-Garonne, France.
| | - Max Pietzker
- Department of Psychology and Ergonomics, Technical University Berlin, Strafte des 17.Juni 135, 10623 Berlin, Berlin, 10623 Berlin, Germany
| | - Dietrich Manzey
- Department of Psychology and Ergonomics, Technical University Berlin, Strafte des 17.Juni 135, 10623 Berlin, Berlin, 10623 Berlin, Germany
| | - Frederic Dehais
- Human Factors and Neuroergonomics, ISAE-SUPAERO, 10 Av. Edouard Belin, Toulouse 31400, Haute-Garonne, France; School of Biomedical Engineering, Science Health Systems, Drexel University, 3141 Chestnut St, Philadelphia 19104, PA, United States
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Correia G, Crosse MJ, Lopez Valdes A. Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1226. [PMID: 38400384 PMCID: PMC10893377 DOI: 10.3390/s24041226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.
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Affiliation(s)
- Guilherme Correia
- Department of Physics, NOVA School of Science and Technology, 2829-516 Caparica, Portugal;
| | - Michael J. Crosse
- Segotia, H91 HE9E Galway, Ireland;
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
| | - Alejandro Lopez Valdes
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Department of Electronic and Electrical Engineering, Trinity College Dublin, D02 Dublin, Ireland
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9
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Yarici M, Von Rosenberg W, Hammour G, Davies H, Amadori P, Ling N, Demiris Y, Mandic DP. Hearables: feasibility of recording cardiac rhythms from single in-ear locations. ROYAL SOCIETY OPEN SCIENCE 2024; 11:221620. [PMID: 38179073 PMCID: PMC10762432 DOI: 10.1098/rsos.221620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
The ear is well positioned to accommodate both brain and vital signs monitoring, via so-called hearable devices. Consequently, ear-based electroencephalography has recently garnered great interest. However, despite the considerable potential of hearable based cardiac monitoring, the biophysics and characteristic cardiac rhythm of ear-based electrocardiography (ECG) are not yet well understood. To this end, we map the cardiac potential on the ear through volume conductor modelling and measurements on multiple subjects. In addition, in order to demonstrate real-world feasibility of in-ear ECG, measurements are conducted throughout a long-time simulated driving task. As a means of evaluation, the correspondence between the cardiac rhythms obtained via the ear-based and standard Lead I measurements, with respect to the shape and timing of the cardiac rhythm, is verified through three measures of similarity: the Pearson correlation, and measures of amplitude and timing deviations. A high correspondence between the cardiac rhythms obtained via the ear-based and Lead I measurements is rigorously confirmed through agreement between simulation and measurement, while the real-world feasibility was conclusively demonstrated through efficacious cardiac rhythm monitoring during prolonged driving. This work opens new avenues for seamless, hearable-based cardiac monitoring that extends beyond heart rate detection to offer cardiac rhythm examination in the community.
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Affiliation(s)
- Metin Yarici
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Wilhelm Von Rosenberg
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Ghena Hammour
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Harry Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Pierluigi Amadori
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Nico Ling
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Yiannis Demiris
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Danilo P. Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
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Zhang J, Li J, Huang Z, Huang D, Yu H, Li Z. Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review. HEALTH DATA SCIENCE 2023; 3:0096. [PMID: 38487198 PMCID: PMC10880169 DOI: 10.34133/hds.0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/19/2023] [Indexed: 03/17/2024]
Abstract
Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.
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Affiliation(s)
- Jiayan Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
| | - Junshi Li
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
| | - Zhe Huang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
- Shenzhen Graduate School,
Peking University, Shenzhen, China
| | - Dong Huang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
- School of Electronics,
Peking University, Beijing, China
| | - Huaiqiang Yu
- Sichuan Institute of Piezoelectric and Acousto-optic Technology, Chongqing, China
| | - Zhihong Li
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
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Mohamed M, Mohamed N, Kim JG. Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review. BIOSENSORS 2023; 13:1019. [PMID: 38131779 PMCID: PMC10741861 DOI: 10.3390/bios13121019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.
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Affiliation(s)
| | | | - Jae Gwan Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea; (M.M.); (N.M.)
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12
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Arao H, Suwazono S, Kimura A, Asano H, Suzuki H. Measuring auditory event-related potentials at the external ear canal: A demonstrative study using a new electrode and error-feedback paradigm. Eur J Neurosci 2023; 58:4310-4327. [PMID: 37875165 DOI: 10.1111/ejn.16175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 10/26/2023]
Abstract
Although ear canal electroencephalogram (EEG) recording has received interest from basic and applied research communities, evidence on how it can be implemented in practice is limited. The present study involving eight male participants including the authors presents the utility of our ear canal electrode and method by demonstrating both comparability of ear canal EEG to those at nearby sites and distinctiveness that ear canal event-related potentials (ERPs) could have. For this purpose, we used the balanced noncephalic electrode reference and an experimental paradigm with an error-feedback sound. Clear auditory ERPs were detected at the ear canal sites with a sufficiently low noise level comparable with those at conventional sites. The N1c, a temporal maximum subcomponent, spread over the bilateral temporal sites, including the ear canals and earlobes. While consecutive signals are generally highly similar between the ear canal and the earlobe, the N1c was larger at the ear canal than the earlobe, as demonstrated by the conventional frequentist and the hierarchical Bayesian modelling approaches. Although an evident caveat is that our sample was limited in terms of size and sex, the general capability indicates that the structure of our ear canal electrode provides EEG measurement that can be used in basic and applied settings. Our experimental method can also be an ERP-based test that conveniently assesses the capability of existing and future ear canal electrodes. The distinctive nature of the ERPs to the error-feedback sound may be utilized to examine the basic aspects of auditory ERPs and to test the processes involved in feedback-guided behaviour of participants.
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Affiliation(s)
- Hiroshi Arao
- Department of Human Sciences, Taisho University, Tokyo, Japan
| | - Shugo Suwazono
- Department of Neurology and Center for Clinical Neuroscience, National Hospital Organization Okinawa National Hospital, Ginowan, Japan
| | | | - Hirotoshi Asano
- Department of Computer Science, Kogakuin University, Tokyo, Japan
| | - Hiroaki Suzuki
- Department of Human Sciences, Taisho University, Tokyo, Japan
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13
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Wang Z, Graci V, Seacrist T, Guez A, Keshner EA. Localizing EEG Recordings Associated With a Balance Threat During Unexpected Postural Translations in Young and Elderly Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4514-4520. [PMID: 37938961 PMCID: PMC10683785 DOI: 10.1109/tnsre.2023.3331211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Balance perturbations are accompanied by global cortical activation that increases in magnitude when postural perturbations are unexpected, potentially due to the addition of a startle response. A specific site for best recording the response to unexpected destabilization has not been identified. We hypothesize that a single sensor located near to subcortical brainstem mechanisms could serve as a marker for the response to unpredictable postural events. Twenty healthy young (20.8 ± 2.9 yrs) and 20 healthy elder (71.7 ± 4.2 yrs) adults stood upright on a dynamic platform with eyes open. Platform translations (20 cm at 100 cm/s) were delivered in the posterior (29 trials) and anterior (5 catch trials) directions. Active EEG electrodes were located at Fz and Cz and bilaterally on the mastoids. Following platform acceleration onset, 300 ms of EEG activity from each trial was detrended, baseline-corrected, and normalized to the first trial. Average Root-Mean-Square (RMS) values across "unpredictable" and "predictable" events were computed for each channel. EEG RMS responses were significantly greater with unpredictable than predictable disturbances: Cz ( [Formula: see text]), Fz ( [Formula: see text]), and mastoid ( [Formula: see text]). EEG RMS responses were also significantly greater in elderly than young adults at Cz ( [Formula: see text]) and mastoid ( [Formula: see text]). A significant effect of sex in the responses at the mastoid sensors ( [Formula: see text]) revealed that elderly male adults were principally responsible for the age effect. These results confirm that the cortical activity resulting from an unexpected postural disturbance could be portrayed by a single sensor located over the mastoid bone in both young and elderly adults.
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Wang J, Wang T, Liu H, Wang K, Moses K, Feng Z, Li P, Huang W. Flexible Electrodes for Brain-Computer Interface System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211012. [PMID: 37143288 DOI: 10.1002/adma.202211012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/27/2023] [Indexed: 05/06/2023]
Abstract
Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body, and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided.
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Affiliation(s)
- Junjie Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Tengjiao Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Haoyan Liu
- Department of Computer Science & Computer Engineering (CSCE), University of Arkansas, Fayetteville, AR, 72701, USA
| | - Kun Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Kumi Moses
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Zhuoya Feng
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Peng Li
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
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Kaongoen N, Choi J, Woo Choi J, Kwon H, Hwang C, Hwang G, Kim BH, Jo S. The future of wearable EEG: a review of ear-EEG technology and its applications. J Neural Eng 2023; 20:051002. [PMID: 37748474 DOI: 10.1088/1741-2552/acfcda] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/25/2023] [Indexed: 09/27/2023]
Abstract
Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording 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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Affiliation(s)
- Netiwit Kaongoen
- Information and Electronics Research Institute, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jaehoon Choi
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jin Woo Choi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States of America
| | - Haram Kwon
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Chaeeun Hwang
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Guebin Hwang
- Robotics Program, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Byung Hyung Kim
- Department of Artificial Intelligence, Inha University, Incheon, Republic of Korea
| | - Sungho Jo
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Musaeus CS, Kjaer TW, Cacic Hribljan M, Andersen BB, Høgh P, Kidmose P, Fabricius M, Hemmsen MC, Rank ML, Waldemar G, Frederiksen KS. Subclinical Epileptiform Activity in Dementia with Lewy Bodies. Mov Disord 2023; 38:1861-1870. [PMID: 37431847 DOI: 10.1002/mds.29531] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB. OBJECTIVES To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC). METHODS In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months. RESULTS At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night. CONCLUSIONS Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Christian Sandøe Musaeus
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Troels Wesenberg Kjaer
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Peter Høgh
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Martin Fabricius
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Gunhild Waldemar
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
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Petrossian G, Kateb P, Miquet-Westphal F, Cicoira F. Advances in Electrode Materials for Scalp, Forehead, and Ear EEG: A Mini-Review. ACS APPLIED BIO MATERIALS 2023; 6:3019-3032. [PMID: 37493408 DOI: 10.1021/acsabm.3c00322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Electroencephalogram (EEG) records the electrical activity of neurons in the cerebral cortex and is used extensively to diagnose, treat, and monitor psychiatric and neurological conditions. Reliable contact between the skin and the electrodes is essential for achieving consistency and for obtaining electroencephalographic information. There has been an increasing demand for effective equipment and electrodes to overcome the time-consuming and cumbersome application of traditional systems. Recently, ear-centered EEG has met with growing interest since it can provide good signal quality due to the proximity of the ear to the brain. In addition, it can facilitate mobile and unobtrusive usage due to its smaller size and ease of use, since it can be used without interfering with the patient's daily activities. The purpose of this mini-review is to first introduce the broad range of electrodes used in conventional (scalp) EEG and subsequently discuss the state-of-the-art literature about around- and in-the-ear EEG.
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Affiliation(s)
- Gayaneh Petrossian
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | - Pierre Kateb
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | | | - Fabio Cicoira
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
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Hammour G, Atzori G, Monica CD, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083340 DOI: 10.1109/embc40787.2023.10340253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.
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Lee MS, Paul A, Joung TH, Xu Y, Wu J, Hairston WD, Cauwenberghs G. Scalable Anatomically-Tunable Fully In-Ear Dry-Electrode Array for User-Generic Unobtrusive Electrophysiology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082718 DOI: 10.1109/embc40787.2023.10340888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Traditional scalp EEG instrumentation is bulky and arduous to set up, requiring wires that constrain the subject's movement, conductive wet gels that dry over time which limits long-term recording, and/or is socially stigmatized. Therefore, there is growing research in in-ear EEG to increase user wearability, ease of use, and concealability. However, the fabrication of in-ear EEG sensors utilizes complex equipment and materials to capture the intricate geometry of the ear and to fabricate custom earpieces and electrodes. This work aims to lower the barrier of entry by decreasing the fabrication complexity by using PCB components with versatile, user-generic designs. Measured results on the assembled earpiece demonstrate that it viably captures eye blinks, jaw clench, auditory steady-state response (ASSR), and alpha modulation. Additionally, electrochemical impedance spectroscopy (EIS) experiments show reliable electrode-skin contact with impedance comparable to conventional dry-electrode designs at substantially greater channel density.
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Paul A, Lee MS, Xu Y, Deiss SR, Cauwenberghs G. A Versatile In-Ear Biosensing System and Body-Area Network for Unobtrusive Continuous Health Monitoring. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:483-494. [PMID: 37134030 PMCID: PMC10550504 DOI: 10.1109/tbcas.2023.3272649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
To enable continuous, mobile health monitoring, body-worn sensors need to offer comparable performance to clinical devices in a lightweight, unobtrusive package. This work presents a complete versatile wireless electrophysiology data acquisition system (weDAQ) that is demonstrated for in-ear electroencephalography (EEG) and other on-body electrophysiology with user-generic dry-contact electrodes made from standard printed circuit boards (PCBs). Each weDAQ device provides 16 recording channels, driven right leg (DRL), a 3-axis accelerometer, local data storage, and adaptable data transmission modes. The weDAQ wireless interface supports deployment of a body area network (BAN) capable of aggregating various biosignal streams over multiple worn devices simultaneously, on the 802.11n WiFi protocol. Each channel resolves biopotentials ranging over 5 orders of magnitude with a noise level of 0.52 μVrms over a 1000-Hz bandwidth, and a peak SNDR of 119 dB and CMRR of 111 dB at 2 ksps. The device leverages in-band impedance scanning and an input multiplexer to dynamically select good skin contacting electrodes for reference and sensing channels. In-ear and forehead EEG measurements taken from subjects captured modulation of alpha brain activity, electrooculogram (EOG) characteristic eye movements, and electromyogram (EMG) from jaw muscles. Simultaneous ECG and EMG measurements were demonstrated on multiple, freely-moving subjects in their natural office environment during periods of rest and exercise. The small footprint, performance, and configurability of the open-source weDAQ platform and scalable PCB electrodes presented, aim to provide the biosensing community greater experimental flexibility and lower the barrier to entry for new health monitoring research.
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Musaeus CS, Frederiksen KS, Andersen BB, Høgh P, Kidmose P, Fabricius M, Hribljan MC, Hemmsen MC, Rank ML, Waldemar G, Kjær TW. Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring. Neurobiol Dis 2023; 183:106149. [PMID: 37196736 DOI: 10.1016/j.nbd.2023.106149] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/26/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC). METHODS In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months. RESULTS The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77-5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings. CONCLUSIONS Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.
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Affiliation(s)
- Christian Sandøe Musaeus
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Peter Høgh
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
| | - Martin Fabricius
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Troels Wesenberg Kjær
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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22
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Hammour G, Mandic DP. An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063319. [PMID: 36992029 PMCID: PMC10057625 DOI: 10.3390/s23063319] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 06/12/2023]
Abstract
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.
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23
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Yarici MC, Thornton M, Mandic DP. Ear-EEG sensitivity modeling for neural sources and ocular artifacts. Front Neurosci 2023; 16:997377. [PMID: 36699519 PMCID: PMC9868963 DOI: 10.3389/fnins.2022.997377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
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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Del Percio C, Lopez S, Noce G, Lizio R, Tucci F, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Marizzoni M, Güntekin B, Yener G, Stocchi F, Vacca L, Frisoni GB, Babiloni C. What a Single Electroencephalographic (EEG) Channel Can Tell us About Alzheimer's Disease Patients With Mild Cognitive Impairment. Clin EEG Neurosci 2023; 54:21-35. [PMID: 36413420 DOI: 10.1177/15500594221125033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abnormalities in cortical sources of resting-state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10-20 montage) with 19 scalp electrodes characterized Alzheimer's disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluation of AD progression in large populations with few electrodes in low-cost devices. Here we evaluated whether the above-mentioned abnormalities can be observed from fewer scalp electrodes in patients with mild cognitive impairment due to AD (ADMCI). Clinical and rsEEG data acquired in hospital settings (10-20 montage) from 75 ADMCI participants and 70 age-, education-, and sex-matched normal elderly controls (Nold) were available in an Italian-Turkish archive (PDWAVES Consortium; www.pdwaves.eu). Standard spectral fast fourier transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3-P3, C4-P4, P3-O1, and P4-O2 markers. The ADMCI group showed increased delta and decreased alpha power density at the C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels compared to the Nold group. Increased theta power density for ADMCI patients was observed only at the C3-P3 bipolar channel. Best classification accuracy between the ADMCI and Nold individuals reached 81% (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel. Standard rsEEG power density computed from six posterior bipolar channels characterized ADMCI status. These results may pave the way toward diffuse clinical applications in health monitoring of dementia using low-cost EEG systems with a strict number of electrodes in lower- and middle-income countries.
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Affiliation(s)
- Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | | | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Flavio Nobili
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, 218502Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab., 218502Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and 27212University of Geneva, Geneva, Switzerland
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy.,Hospital San Raffaele Cassino, Cassino (FR), Italy
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25
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Musaeus CS, Waldemar G, Andersen BB, Høgh P, Kidmose P, Hemmsen MC, Rank ML, Kjær TW, Frederiksen KS. Long-Term EEG Monitoring in Patients with Alzheimer's Disease Using Ear-EEG: A Feasibility Study. J Alzheimers Dis 2022; 90:1713-1723. [PMID: 36336927 DOI: 10.3233/jad-220491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Previous studies have reported that epileptiform activity may be detectible in nearly half of patients with Alzheimer's disease (AD) on long-term electroencephalographic (EEG) recordings. However, such recordings can be uncomfortable, expensive, and difficult. Ear-EEG has shown promising results for long-term EEG monitoring, but it has not been used in patients with AD. OBJECTIVE To investigate if ear-EEG is a feasible method for long-term EEG monitoring in patients with AD. METHODS In this longitudinal, single-group feasibility study, ten patients with mild to moderate AD were recruited. A total of three ear-EEG recordings of up to 48 hours three months apart for six months were planned. RESULTS All patients managed to wear the ear-EEG for at least 24 hours and at least one full night. A total of 19 ear-EEG recordings were performed (self-reported recording, mean: 37.15 hours (SD: 8.96 hours)). After automatic pre-processing, a mean of 27.37 hours (SD: 7.19 hours) of data with acceptable quality in at least one electrode in each ear was found. Seven out of ten participants experienced mild adverse events. Six of the patients did not complete the study with three patients not wanting to wear the ear-EEG anymore due to adverse events. CONCLUSION It is feasible and safe to use ear-EEG for long-term EEG monitoring in patients with AD. Minor adjustments to the equipment may improve the comfort for the participants.
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Affiliation(s)
- Christian Sandøe Musaeus
- Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark
| | - Gunhild Waldemar
- Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark
| | - Peter Høgh
- Department of Neurology, Regional Dementia Research Centre, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
| | | | | | - Troels Wesenberg Kjær
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark
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26
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Kim M, Yoo S, Kim C. Miniaturization for wearable EEG systems: recording hardware and data processing. Biomed Eng Lett 2022; 12:239-250. [PMID: 35692891 PMCID: PMC9168644 DOI: 10.1007/s13534-022-00232-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 12/05/2022] Open
Abstract
As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.
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Affiliation(s)
- Minjae Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, Daejeon, 34141 Republic of Korea
| | - Seungjae Yoo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, Daejeon, 34141 Republic of Korea
| | - Chul Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, Daejeon, 34141 Republic of Korea
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daehak-ro, Daejeon, 34141 Republic of Korea
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27
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Rahman MM, Xu X, Nathan V, Ahmed T, Ahmed MY, McCaffrey D, Kuang J, Cowell T, Moore J, Mendes WB, Gao JA. Detecting Physiological Responses Using Multimodal Earbud Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1-5. [PMID: 36085850 DOI: 10.1109/embc48229.2022.9871569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous stress exposure negatively impacts mental and physical well-being. Physiological arousal due to stress affects heartbeat frequency, changes breathing pattern and peripheral temperature, among several other bodily responses. Traditionally stress detection is performed by collecting signals such as electrocardiogram (ECG), respiration, and skin conductance response using uncomfortable sensors such as a chestband. In this study, we use earbuds that passively measure photoplethysmography (PPG), core body temperature, and inertial measurements. We have conducted a lab study exposing 18 participants to an evaluated speech task and additional tasks aimed at increasing stress or promoting relaxation. We simultaneously collected PPG, ECG, impedance cardiography (ICG), and blood pressure using laboratory grade equipment as reference measurements. We show that the earbud PPG sensor can reliably capture heart rate and heart rate variability. We further show that earbud signals can be used to classify the physiological responses associated with stress with 91.30% recall, 80.52% precision, and 85.12% F1-score using a random forest classifier with leave-one-subject-out cross-validation. The accuracy can further be improved through multi-modal sensing. These findings demonstrate the feasibility of using earbuds for passively monitoring users' physiological responses.
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28
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Guermandi M, Cossettini A, Benatti S, Benini L. A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3139-3145. [PMID: 36086587 DOI: 10.1109/embc48229.2022.9871874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.
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29
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Das A, Basu S, A A, Gubbi J, Muralidharan K, S M, S M, Biradar A, Pradhan U, Chakravarty T, Ramakrishnan RK, Pal A. Surface Potential Simulation and Electrode Design for in-Ear EEG Measurement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:937-940. [PMID: 36086437 DOI: 10.1109/embc48229.2022.9871926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The need for everyday-real-time EEG sensing has resulted in the development of minimalistic and discreet wearable EEG measuring devices. A front runner in this race is in-ear worn device. However, the position of the ear as well as its restricted accessibility poses certain challenges in the design of such devices - from the type of materials used to the placement of electrodes. These choices are important as they will determine the quality of the EEG signal available and in turn the accuracy of the application. We therefore create a simulation model of the human ear, allowing us to understand the impact of these choices on our design of an In-Ear EEG wearable. We first study the signal acquisition characteristics of a proposed gold-plated electrode against two other state-of-the-art electrode materials for in-ear EEG data collection. We further validate this electrode choice by fabricating a personalized silicone-based earpiece and collecting in-situ EEG data. This work explores the properties of using gold plated electrodes in capturing in-ear EEG signals enabling unobtrusive collection of the brain physiology data in real world setting.
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30
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Israsena P, Pan-Ngum S. A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG. Front Comput Neurosci 2022; 16:868642. [PMID: 35664916 PMCID: PMC9160186 DOI: 10.3389/fncom.2022.868642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.
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Affiliation(s)
- Pasin Israsena
- National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, Thailand
- *Correspondence: Pasin Israsena
| | - Setha Pan-Ngum
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
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31
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Holtze B, Rosenkranz M, Jaeger M, Debener S, Mirkovic B. Ear-EEG Measures of Auditory Attention to Continuous Speech. Front Neurosci 2022; 16:869426. [PMID: 35592265 PMCID: PMC9111016 DOI: 10.3389/fnins.2022.869426] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Auditory attention is an important cognitive function used to separate relevant from irrelevant auditory information. However, most findings on attentional selection have been obtained in highly controlled laboratory settings using bulky recording setups and unnaturalistic stimuli. Recent advances in electroencephalography (EEG) facilitate the measurement of brain activity outside the laboratory, and around-the-ear sensors such as the cEEGrid promise unobtrusive acquisition. In parallel, methods such as speech envelope tracking, intersubject correlations and spectral entropy measures emerged which allow us to study attentional effects in the neural processing of natural, continuous auditory scenes. In the current study, we investigated whether these three attentional measures can be reliably obtained when using around-the-ear EEG. To this end, we analyzed the cEEGrid data of 36 participants who attended to one of two simultaneously presented speech streams. Speech envelope tracking results confirmed a reliable identification of the attended speaker from cEEGrid data. The accuracies in identifying the attended speaker increased when fitting the classification model to the individual. Artifact correction of the cEEGrid data with artifact subspace reconstruction did not increase the classification accuracy. Intersubject correlations were higher for those participants attending to the same speech stream than for those attending to different speech streams, replicating previously obtained results with high-density cap-EEG. We also found that spectral entropy decreased over time, possibly reflecting the decrease in the listener's level of attention. Overall, these results support the idea of using ear-EEG measurements to unobtrusively monitor auditory attention to continuous speech. This knowledge may help to develop assistive devices that support listeners separating relevant from irrelevant information in complex auditory environments.
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Affiliation(s)
- Björn Holtze
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Marc Rosenkranz
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Manuela Jaeger
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Division Hearing, Speech and Audio Technology, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Research Center for Neurosensory Science, University of Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - Bojana Mirkovic
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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32
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Wang Y, Zhang X, Chakalasiya JM, Xu X, Jiang Y, Li Y, Patel S, Shi Y. HearCough: Enabling Continuous Cough Event Detection on the Edge Computing Hearables. Methods 2022; 205:53-62. [DOI: 10.1016/j.ymeth.2022.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/09/2022] [Accepted: 05/07/2022] [Indexed: 10/18/2022] Open
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33
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Choi SI, Lee JY, Lim KM, Hwang HJ. Evaluation of Real-Time Endogenous Brain-Computer Interface Developed Using Ear-Electroencephalography. Front Neurosci 2022; 16:842635. [PMID: 35401092 PMCID: PMC8987155 DOI: 10.3389/fnins.2022.842635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/03/2022] [Indexed: 11/13/2022] Open
Abstract
While previous studies have demonstrated the feasibility of using ear-electroencephalography (ear-EEG) for the development of brain-computer interfaces (BCIs), most of them have been performed using exogenous paradigms in offline environments. To verify the reliable feasibility of constructing ear-EEG-based BCIs, the feasibility of using ear-EEG should be further demonstrated using another BCI paradigm, namely the endogenous paradigm, in real-time online environments. Exogenous and endogenous BCIs are to use the EEG evoked by external stimuli and induced by self-modulation, respectively. In this study, we investigated whether an endogenous ear-EEG-based BCI with reasonable performance can be implemented in online environments that mimic real-world scenarios. To this end, we used three different mental tasks, i.e., mental arithmetic, word association, and mental singing, and performed BCI experiments with fourteen subjects on three different days to investigate not only the reliability of a real-time endogenous ear-EEG-based BCI, but also its test-retest reliability. The mean online classification accuracy was almost 70%, which was equivalent to a marginal accuracy for a practical two-class BCI (70%), demonstrating the feasibility of using ear-EEG for the development of real-time endogenous BCIs, but further studies should follow to improve its performance enough to be used for practical ear-EEG-based BCI applications.
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Affiliation(s)
- Soo-In Choi
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, South Korea
| | - Ji-Yoon Lee
- Department of Electronics and Information Engineering, Korea University, Sejong City, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong City, South Korea
| | - Ki Moo Lim
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, South Korea
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, South Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information Engineering, Korea University, Sejong City, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong City, South Korea
- *Correspondence: Han-Jeong Hwang,
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34
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Henao D, Navarrete M, Juez JY, Dinh H, Gómez R, Valderrama M, Le Van Quyen M. Auditory closed‐loop stimulation on sleep slow oscillations using in‐ear EEG sensors. J Sleep Res 2022; 31:e13555. [DOI: 10.1111/jsr.13555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/30/2022]
Affiliation(s)
- David Henao
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Miguel Navarrete
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
- Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff UK
| | - José Yesith Juez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | | | - Rodrigo Gómez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Mario Valderrama
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Michel Le Van Quyen
- Laboratoire d’Imagerie Biomédicale (LIB) Inserm U1146/Sorbonne Université UMCR2/UMR7371 CNRS Paris France
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35
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Davies HJ, Bachtiger P, Williams I, Molyneaux PL, Peters NS, Mandic DP. Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD. IEEE Trans Biomed Eng 2022; 69:2390-2400. [PMID: 35077352 DOI: 10.1109/tbme.2022.3145688] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
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36
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Hong JW, Yoon C, Jo K, Won JH, Park S. Recent advances in recording and modulation technologies for next-generation neural interfaces. iScience 2021; 24:103550. [PMID: 34917907 PMCID: PMC8666678 DOI: 10.1016/j.isci.2021.103550] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Along with the advancement in neural engineering techniques, unprecedented progress in the development of neural interfaces has been made over the past few decades. However, despite these achievements, there is still room for further improvements especially toward the possibility of monitoring and modulating neural activities with high resolution and specificity in our daily lives. In an effort of taking a step toward the next-generation neural interfaces, we want to highlight the recent progress in neural technologies. We will cover a wide scope of such developments ranging from novel platforms for highly specific recording and modulation to system integration for practical applications of novel interfaces.
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Affiliation(s)
- Ji-Won Hong
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chanwoong Yoon
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kyunghyun Jo
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Joon Hee Won
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seongjun Park
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.,KAIST Institute of Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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Ne CKH, Muzaffar J, Amlani A, Bance M. Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review. Expert Rev Med Devices 2021; 18:95-128. [PMID: 34904507 DOI: 10.1080/17434440.2021.2014321] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Hearables are ear devices used for multiple purposes including ubiquitous/remote monitoring of vital signals. This can support early detection, prevention, and management of urgent/non-urgent healthcare needs. This review therefore seeks to analyse the challenges and capabilities of hearables used to monitor human physiological signals. AREAS COVERED Studies were identified via search (Medline, Embase, Web of Science, Cochrane Library, Scopus) and conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Bias assessment used the Mixed Methods Appraisal Tool 2018 and Quality Assessment of Diagnostic Accuracy Studies 2nd Edition. 92/631 studies met the inclusion criteria and were qualitatively analysed. The outcomes, applications, advantages and limitations were discussed according to the vital signal measured. The bias risk ranged from low to high, with most studies facing moderate to high risk in subject selection due to small sample sizes. EXPERT OPINION : Most studies reported good outcomes for ear signal acquisition compared to reference devices. To improve practicability and implementation, wireless connectivity, battery life, impact of motion/environmental artifacts and comfort need to be addressed going forward. Hearable technologies have also shown potential synergies with hearing aids. In future, multimodal ear-sensing devices opens the possibility of comprehensive health monitoring within daily life.
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Affiliation(s)
| | - Jameel Muzaffar
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Aakash Amlani
- Department of Ear, Nose and Throat Surgery, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Manohar Bance
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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Hammour GM, Mandic DP. Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6889-6893. [PMID: 34892689 DOI: 10.1109/embc46164.2021.9629886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ear-worn devices are rapidly gaining popularity as they provide the means for measuring vital signals in an unobtrusive, 24/7 wearable and discrete fashion. Naturally, these devices are prone to motion artefacts when used in out-of-lab environments, various movements and activities cause relative movement between user's skin and the electrodes. Historically, these artefacts are seen as nuisance resulting in discarding the segments of signal wherever such artefacts are present. In this work, we make use of such artefacts to classify different daily activities that include sitting, speaking aloud, chewing and walking. To this end, multiple classification techniques are employed to identify these activities using 8 features calculated from the electrode and microphone signal embedded in a generic multimodal in-ear sensor. The results show an overall training accuracy of 93% and 90% and a testing accuracy of 85% and 80% when using a KNN and a 2-layer neural network respectively, thus providing a much needed, simple and reliable framework for real-life human activity classification.
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Mundanad Narayanan A, Zink R, Bertrand A. EEG miniaturization limits for stimulus decoding with EEG sensor networks. J Neural Eng 2021; 18. [PMID: 34517358 DOI: 10.1088/1741-2552/ac2629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/13/2021] [Indexed: 11/12/2022]
Abstract
Objective. Unobtrusive electroencephalography (EEG) monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance.Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding.Main results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm.Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.
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Affiliation(s)
- Abhijith Mundanad Narayanan
- KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.,Leuven.AI-KU Leuven institute for AI, B-3000 Leuven, Belgium
| | - Rob Zink
- KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
| | - Alexander Bertrand
- KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.,Leuven.AI-KU Leuven institute for AI, B-3000 Leuven, Belgium
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40
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Geravanchizadeh M, Zakeri S. Ear-EEG-based binaural speech enhancement (ee-BSE) using auditory attention detection and audiometric characteristics of hearing-impaired subjects. J Neural Eng 2021; 18. [PMID: 34289464 DOI: 10.1088/1741-2552/ac16b4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 07/21/2021] [Indexed: 11/11/2022]
Abstract
Objective. Speech perception in cocktail party scenarios has been the concern of a group of researchers who are involved with the design of hearing-aid devices.Approach. In this paper, a new unified ear-EEG-based binaural speech enhancement system is introduced for hearing-impaired (HI) listeners. The proposed model, which is based on auditory attention detection (AAD) and individual hearing threshold (HT) characteristics, has four main processing stages. In the binaural processing stage, a system based on the deep neural network is trained to estimate auditory ratio masks for each of the speakers in the mixture signal. In the EEG processing stage, AAD is employed to select one ratio mask corresponding to the attended speech. Here, the same EEG data is also used to predict the HTs of listeners who participated in the EEG recordings. The third stage, called insertion gain computation, concerns the calculation of a special amplification gain based on individual HTs. Finally, in the selection-resynthesis-amplification stage, the attended speech signals of the target are resynthesized based on the selected auditory mask and then are amplified using the computed insertion gain.Main results. The detection of the attended speech and the HTs are achieved by classifiers that are trained with features extracted from the scalp EEG or the ear EEG signals. The results of evaluating AAD and HT detection show high detection accuracies. The systematic evaluations of the proposed system yield substantial intelligibility and quality improvements for the HI and normal-hearingaudiograms.Significance. The AAD method determines the direction of attention from single-trial EEG signals without access to audio signals of the speakers. The amplification procedure could be adjusted for each subject based on the individual HTs. The present model has the potential to be considered as an important processing tool to personalize the neuro-steered hearing aids.
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Affiliation(s)
- Masoud Geravanchizadeh
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-15813, Iran
| | - Sahar Zakeri
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-15813, Iran
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41
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BechChristensen C, Lunner T, Harte J, Rank M, Kidmose P. Chirp-evoked auditory steady-state response: The effect of repetition rate. IEEE Trans Biomed Eng 2021; 69:689-699. [PMID: 34383641 DOI: 10.1109/tbme.2021.3103332] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE The auditory steady-state response (ASSR) is commonly used in clinical pediatric audiology in order to provide an electrophysiological estimate of hearing threshold, and has the potential to be used in unsupervised mobile EEG applications. Enhancement of the ASSR amplitude through optimization of the stimulation and recording methods shortens the required testing time or reduce the offset between the electrophysiological and behavioral thresholds. Here, we investigate the spatial distribution of the ASSR to broadband chirp stimuli across a wide range of repetition rates on the scalp and in the ears. Moreover, the ASSR amplitude is compared across repetition rates for commonly used electrode configurations. METHODS ASSR to chirp stimuli with repetition rates from 6-198 Hz was recorded using scalp EEG and high-density ear-EEG. RESULTS The distributions of the ASSR amplitude and phase were found to be dependent on the chirp repetition rate across the scalp, but independent of repetition rate in the ears. The normal drop in ASSR SNR for high repetition rates seen for click and pure tone stimuli was not found for chirp stimuli. Instead, the ASSR SNRs for chirp stimuli at high repetition rates (95-198 Hz) were found to be comparable to that found at 40 Hz for scalp EEG and even higher than 40 Hz ASSR for ear-EEG. CONCLUSION Based on the results, use of chirp stimuli with high repetition rates (95-198 Hz) is advantageous for multiple stimulus ASSR recording in both clinical practice and mobile real-life applications.
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Ruhnau P, Zaehle T. Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation. Front Hum Neurosci 2021; 15:699473. [PMID: 34194308 PMCID: PMC8236702 DOI: 10.3389/fnhum.2021.699473] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 05/21/2021] [Indexed: 11/17/2022] Open
Abstract
No matter how hard we concentrate, our attention fluctuates – a fact that greatly affects our success in completing a current task. Here, we review work from two methods that, in a closed-loop manner, have the potential to ameliorate these fluctuations. Ear-EEG can measure electric brain activity from areas in or around the ear, using small and thus portable hardware. It has been shown to capture the state of attention with high temporal resolution. Transcutaneous auricular vagus nerve stimulation (taVNS) comes with the same advantages (small and light) and critically current research suggests that it is possible to influence ongoing brain activity that has been linked to attention. Following the review of current work on ear-EEG and taVNS we suggest that a combination of the two methods in a closed-loop system could serve as a potential application to modulate attention.
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Affiliation(s)
- Philipp Ruhnau
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
| | - Tino Zaehle
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
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Seok D, Lee S, Kim M, Cho J, Kim C. Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems. FRONTIERS IN ELECTRONICS 2021. [DOI: 10.3389/felec.2021.685513] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
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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Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area. SENSORS 2021; 21:s21041255. [PMID: 33578747 PMCID: PMC7916503 DOI: 10.3390/s21041255] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/20/2021] [Accepted: 02/04/2021] [Indexed: 11/24/2022]
Abstract
Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.
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45
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Lee YE, Kwak NS, Lee SW. A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2021; 28:2660-2670. [PMID: 33232242 DOI: 10.1109/tnsre.2020.3040264] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Recently, practical brain-computer interfaces (BCIs) have been widely investigated for detecting human intentions in real world. However, performance differences still exist between the laboratory and the real world environments. One of the main reasons for such differences comes from the user's unstable physical states (e.g., human movements are not strictly controlled), which produce unexpected signal artifacts. Hence, to minimize the performance degradation of electroencephalography (EEG)-based BCIs, we present a novel artifact removal method named constrained independent component analysis with online learning (cIOL). The cIOL can find and reject the noise-like components related to human body movements (i.e., movement artifacts) in the EEG signals. To obtain movement information, isolated electrodes are used to block electrical signals from the brain using high-resistance materials. We estimate artifacts with movement information using constrained independent component analysis from EEG signals and then extract artifact-free signals using online learning in each sample. In addition, the cIOL is evaluated by signal processing under 16 different experimental conditions (two types of EEG devices × two BCI paradigms × four different walking speeds). The experimental results show that the cIOL has the highest accuracy in both scalp- and ear-EEG, and has the highest signal-to-noise ratio in scalp-EEG among the state-of-the-art methods, except for the case of steady-state visual evoked potential at 2.0 m/s with superposition problem.
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46
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Wu X, Zhang W, Fu Z, Cheung RTH, Chan RHM. An investigation of in-ear sensing for motor task classification. J Neural Eng 2020; 17. [PMID: 33059338 DOI: 10.1088/1741-2552/abc1b6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/15/2020] [Indexed: 11/12/2022]
Abstract
Our study aims to investigate the feasibility of in-ear sensing for human-computer interface. We first measured the agreement between in-ear biopotential and scalp-EEG signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological signals. The best performance using in-ear biopotential with global reference reached an average accuracy of 70.22\% (cf. 92.61\% accuracy using scalp-EEG signals), but the performance in-ear biopotential with near-ear reference was poor. Our results suggest in-ear sensing would be a viable human-computer interface for movement prediction, but careful consideration should be paid to the position of the reference electrodes.
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Affiliation(s)
- Xiaoli Wu
- MindAmp Limited, Hong Kong, HONG KONG
| | | | - Zhibo Fu
- MindAmp Limited, Hong Kong, HONG KONG
| | - Roy T H Cheung
- Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, HONG KONG
| | - Rosa H M Chan
- City University of Hong Kong Department of Electronic Engineering, Kowloon, HONG KONG
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47
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Iqbal MU, Srinivasan B, Srinivasan R. Dynamic assessment of control room operator's cognitive workload using Electroencephalography (EEG). Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106726] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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48
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Davies HJ, Williams I, Peters NS, Mandic DP. In-Ear SpO 2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4879. [PMID: 32872310 PMCID: PMC7506719 DOI: 10.3390/s20174879] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 02/06/2023]
Abstract
The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.
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Affiliation(s)
- Harry J. Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Ian Williams
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Nicholas S. Peters
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW3 6LY, UK
| | - Danilo P. Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
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The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling. Brain Topogr 2020; 33:665-676. [PMID: 32833181 PMCID: PMC7593286 DOI: 10.1007/s10548-020-00793-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/12/2020] [Indexed: 01/01/2023]
Abstract
Ear-EEG allows to record brain activity in every-day life, for example to study natural behaviour or unhindered social interactions. Compared to conventional scalp-EEG, ear-EEG uses fewer electrodes and covers only a small part of the head. Consequently, ear-EEG will be less sensitive to some cortical sources. Here, we perform realistic electromagnetic simulations to compare cEEGrid ear-EEG with 128-channel cap-EEG. We compute the sensitivity of ear-EEG for different cortical sources, and quantify the expected signal loss of ear-EEG relative to cap-EEG. Our results show that ear-EEG is most sensitive to sources in the temporal cortex. Furthermore, we show how ear-EEG benefits from a multi-channel configuration (i.e. cEEGrid). The pipelines presented here can be adapted to any arrangement of electrodes and can therefore provide an estimate of sensitivity to cortical regions, thereby increasing the chance of successful experiments using ear-EEG.
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50
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Kwak NS, Lee SW. Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3654-3667. [PMID: 31295141 DOI: 10.1109/tcyb.2019.2924237] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework.
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