scholarly journals State of the Art of Non-Invasive Electrode Materials for Brain–Computer Interface

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1521
Author(s):  
Haowen Yuan ◽  
Yao Li ◽  
Junjun Yang ◽  
Hongjie Li ◽  
Qinya Yang ◽  
...  
Keyword(s):  
Electrode Materials ◽  
Material Selection ◽  
Brain Computer Interface ◽  
Great Influence ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Future Expectations ◽  
Non Invasive ◽  
Dry Electrodes

The brain–computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.

scholarly journals Progress in the Field of Micro-Electrocorticography

Micromachines ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 62 ◽  
Author(s):  
Mehdi Shokoueinejad ◽  
Dong-Wook Park ◽  
Yei Jung ◽  
Sarah Brodnick ◽  
Joseph Novello ◽  
...  
Keyword(s):  
Indium Tin Oxide ◽  
Electrode Materials ◽  
Brain Computer Interface ◽  
Electrode Array ◽  
Clinical Pathology ◽  
Computer Interface ◽  
Current Status ◽  
Signal Acquisition ◽  
Electrode Arrays ◽  
Advantages And Disadvantages

Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain–computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain–computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

Neurology ◽  
2018 ◽  
Vol 91 (3) ◽  
pp. e258-e267 ◽  
Author(s):  
Jonathan R. Wolpaw ◽  
Richard S. Bedlack ◽  
Domenic J. Reda ◽  
Robert J. Ringer ◽  
Patricia G. Banks ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Amyotrophic Lateral Sclerosis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
It Use ◽  
Technical Problems ◽  
Rapid Disease Progression ◽  
And Performance ◽  
Lateral Sclerosis

ObjectiveTo assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.MethodsOf 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28–79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.ResultsOver subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.ConclusionThe Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

SSVEP-based Brain-computer Interface for Part-picking Robotic Co-worker

2021 ◽  
pp. 1-13
Author(s):  
Yao Li ◽  
T. Kesavadas
Keyword(s):  
Human Subject ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Industrial Robots ◽  
Computer Interface ◽  
Human Cognition ◽  
Grasp Planning ◽  
Planning Algorithm

Abstract One of the expectations for the next generation of industrial robots is to work collaboratively with humans as robotic co-workers. Robotic co-workers must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We demonstrate a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) which can directly deliver human cognition to robots through a headset. The BCI is applied to a part-picking robot and sends decisions to the robot while operators visually inspecting the quality of parts. The BCI is verified through a human subject study. In the study, a camera by the side of the conveyor takes photos of each part and presents it to the operator automatically. When the operator looks at the photo, the electroencephalography (EEG) is collected through BCI. The inspection decision is extracted through SSVEPs in EEG. When a defective part is identified by the operator, the signal is communicated to the robot which locates the defective part through a second camera and removes it from the conveyor. The robot can grasp various part with our grasp planning algorithm (2FRG). We have developed a CNN-CCA model for SSVEP extraction. The model is trained on a dataset collected in our offline experiment. Our approach outperforms the existing CCA, CCA-SVM, and PSD-SVM models. The CNN-CCA is further validated in an online experiment that achieves 93% accuracy in identifying and removing a defective part.

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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