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EEG Sensors and Electrodes
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EEG Sensors and Electrodes

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 13263

Special Issue Editor

Prof. Dr. Yasue Mitsukura

E-Mail Website
Guest Editor
School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa 223-8522, Japan
Interests: EEG; EMG; EOG; ECG; heart rate; brain–computer interface (BCI); implant-typed BCI; signal processing and so on

Special Issue Information

Dear Colleagues,

In recent years, research on the semantic analysis and understanding of brain signals using biological signals and their application to BCI has been widely conducted. The acquisition of brain signals deals with signals obtained from various devices and machines, such as EEG, NIRS, fMRI, and MEG. We also suffer from noise in the signal. Under such circumstances, performing and establishing these will be an important element for future technology. We will recruit a wide range of papers on brain signal analysis and application, such as EEG signal processing for stress detection, also using various biological signals, i.e., ECG, EMG, EOG, and heart rate.

Prof. Dr. Yasue Mitsukura
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

  • Biological signals 
  • EEG signal processing 
  • ECG(EKG) 
  • ECG 
  • EMG 
  • EOG 
  • NIRS 
  • Brain–computer interface 
  • Emotional detection 
  • Stress analysis 
  • Neuroscience

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Published Papers (3 papers)

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Research

13 pages, 2844 KiB  
Communication
A Portable Waterproof EEG Acquisition Device for Dolphins
by Yanchao Yu, Ni Li, Yan Li and Wentao Liu
Sensors 2021, 21(10), 3336; https://doi.org/10.3390/s21103336 - 11 May 2021
Cited by 8 | Viewed by 3569
Abstract
The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study [...] Read more.
The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study designs a novel, light-weighted, and portable EEG acquisition device aimed at relatively unrestricted EEG acquisition. An embedded main control board and an acquisition board were designed, and all modules are encapsulated in a 162 × 94 × 60 mm3 waterproof device box, which can be tied to the dolphin’s body by a silicon belt. The acquisition device uses customized suction cups with embedded electrodes and adopts a Bluetooth module for wireless communication with the ground station. The sampled signals are written to the memory card on board when the Bluetooth communication is blocked. A limited experiment was designed to verify the effectiveness of the device functionality onshore and underwater. However, more rigorous long-term tests on dolphins in various states with our device are expected in future to further prove its capability and study the movement-related artifacts. Full article
(This article belongs to the Special Issue EEG Sensors and Electrodes)
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21 pages, 1604 KiB  
Article
Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors
by Yasue Mitsukura, Brian Sumali, Masaki Nagura, Koichi Fukunaga and Masato Yasui
Sensors 2020, 20(19), 5688; https://doi.org/10.3390/s20195688 - 5 Oct 2020
Cited by 11 | Viewed by 4211
Abstract
Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this [...] Read more.
Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals. Full article
(This article belongs to the Special Issue EEG Sensors and Electrodes)
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12 pages, 1611 KiB  
Article
Highly Porous Platinum Electrodes for Dry Ear-EEG Measurements
by Max Eickenscheidt, Patrick Schäfer, Yara Baslan, Claudia Schwarz and Thomas Stieglitz
Sensors 2020, 20(11), 3176; https://doi.org/10.3390/s20113176 - 3 Jun 2020
Cited by 13 | Viewed by 3800
Abstract
The interest in dry electroencephalography (EEG) electrodes has increased in recent years, especially as everyday suitability earplugs for measuring drowsiness or focus of auditory attention. However, the challenge is still the need for a good electrode material, which is reliable and can be [...] Read more.
The interest in dry electroencephalography (EEG) electrodes has increased in recent years, especially as everyday suitability earplugs for measuring drowsiness or focus of auditory attention. However, the challenge is still the need for a good electrode material, which is reliable and can be easily processed for highly personalized applications. Laser processing, as used here, is a fast and very precise method to produce personalized electrode configurations that meet the high requirements of in-ear EEG electrodes. The arrangement of the electrodes on the flexible and compressible mats allows an exact alignment to the ear mold and contributes to high wearing comfort, as no edges or metal protrusions are present. For better transmission properties, an adapted coating process for surface enlargement of platinum electrodes is used, which can be controlled precisely. The resulting porous platinum-copper alloy is chemically very stable, shows no exposed copper residues, and enlarges the effective surface area by 40. In a proof-of-principle experiment, these porous platinum electrodes could be used to measure the Berger effect in a dry state using just one ear of a test person. Their signal-to-noise ratio and the frequency transfer function is comparable to gel-based silver/silver chloride electrodes. Full article
(This article belongs to the Special Issue EEG Sensors and Electrodes)
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