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.

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 Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Mamunur Rashid ◽  
Norizam Sulaiman ◽  
Anwar P. P. Abdul Majeed ◽  
Rabiu Muazu Musa ◽  
Ahmad Fakhri Ab. Nasir ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Current Status ◽  
Comprehensive Review

An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs

2013 ◽  
Vol 284-287 ◽  
pp. 1616-1621 ◽  
Author(s):  
Jzau Sgeng Lin ◽  
Sun Ming Huang
Keyword(s):  
Signal Processing ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Processing Unit ◽  
Electric Wheelchair ◽  
Brain Waves ◽  
Compact Size ◽  
Bci System ◽  
Signal Processing Unit

A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip microcontroller to process the EEG signals. In this paper, a simple BCI system with two channels and an FPGA-based circuit for controlling DC motor can help paralytic patients easily to drive the electric wheelchair. The proposed BCI system consists of a wireless physiological with two-channel acquisition module and an FPGA-based signal processing unit. Here, the physiological signal acquisition module and signal processing unit were designed for extracting EEG and winking signals from brain waves which can directly transformed into control signals to drive the electric wheelchairs. The advantages of the proposed BCI system are low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed feasible action for the proposed BCI system and drive circuit with a practical operating in electric wheelchair applications.

scholarly journals A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

2021 ◽  
Vol 39 (7) ◽  
pp. 1117-1132
Author(s):  
Samaa S. Abdulwahab ◽  
Hussain K. Khleaf ◽  
Manal H. Jassim
Keyword(s):  
Brain Computer Interface ◽  
Physical World ◽  
The Body ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Rule Based ◽  
The Past ◽  
Brain Signals ◽  
Translation Systems ◽  
The Brain

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally,  discussed is the application of EEG-based BCI.

scholarly journals Carbon Allotropes as ITO Electrode Replacement Materials in Liquid Crystal Devices

2020 ◽  
Vol 6 (4) ◽  
pp. 80
Author(s):  
Ingo Dierking
Keyword(s):  
Liquid Crystal ◽  
Indium Tin Oxide ◽  
Electrode Materials ◽  
Mobile Technologies ◽  
Scale Production ◽  
Advantages And Disadvantages ◽  
Liquid Crystal Devices ◽  
The Future ◽  
Technical Specifications ◽  
Carbon Based

Indium tin oxide (ITO)-free optoelectronic devices have been discussed for a number of years in the light of a possible indium shortage as demand rises. In particular, this is due to the largely increased number of flat panel displays and especially liquid crystal displays (LCDs) being produced for home entertainment TV and mobile technologies. While a shortage of primary indium seems far on the horizon, nevertheless, recycling has become an important issue, as has the development of ITO-free electrode materials, especially for flexible liquid crystal devices. The main contenders for new electrode technologies are discussed with an emphasis placed on carbon-based materials for LCDs, including composite approaches. At present, these already fulfil the technical specifications demanded from ITO with respect to transmittance and sheet resistance, albeit not in relation to cost and large-scale production. Advantages and disadvantages of ITO-free technologies are discussed, with application examples given. An outlook into the future suggests no immediate transition to carbon-based electrodes in the area of LCDs, while this may change in the future once flexible displays and environmentally friendly smart window solutions or energy harvesting building coverings become available.

scholarly journals Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction

2020 ◽  
Vol 2020 ◽  
pp. 1-2 ◽  
Author(s):  
Hyun Jae Baek ◽  
Hyun Seok Kim ◽  
Minkyu Ahn ◽  
Hohyun Cho ◽  
Sangtae Ahn
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Current Status ◽  
Future Direction

Optimizing the P300-based brain–computer interface: current status, limitations and future directions

2011 ◽  
Vol 8 (2) ◽  
pp. 025003 ◽  
Author(s):  
J N Mak ◽  
Y Arbel ◽  
J W Minett ◽  
L M McCane ◽  
B Yuksel ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Current Status ◽  
Future Directions

scholarly journals Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Nibras Abo Alzahab ◽  
Luca Apollonio ◽  
Angelo Di Iorio ◽  
Muaaz Alshalak ◽  
Sabrina Iarlori ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Computer Interface ◽  
Temporal Frequency ◽  
Computer Interface ◽  
Optimization Approach ◽  
Advantages And Disadvantages ◽  
Number Of Layers ◽  
Temporal Features ◽  
Eeg Data

Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends. Results: Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency. Significance: To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

scholarly journals An Innovative Way by Manipulating Things using the Power of Thought

2019 ◽  
Vol 8 (6S2) ◽  
pp. 439-442
Keyword(s):  
Remote Control ◽  
Brain Computer Interface ◽  
Potential Outcomes ◽  
Computer Interface ◽  
The Moon ◽  
Stroke Patients ◽  
Advantages And Disadvantages ◽  
The Earth ◽  
To Come ◽  
The One

Using the force of thought to control the earth might appear like a thing removed from sci-fi books. Be that as it may, the advancements we see today were once sci-fi beginning from man arriving on the moon to examine in teleportation. Thus controlling the surroundings through musings is likewise one of the apexes of that development called Brain Computer Interface. Utilizing cerebrum waves measured of an EEG to control PC. The contemporary remote control is supplanted by the force of one's idea which couldturn considerations into reality. The application ranges from utilizing contemplations to play diversions to re-wiring of the brain.The numerous unending potential outcomes extrapolated from this innovation could be of Controlling prosthetic appendages, robots, PC and practicing the cerebrum to rewire itself in stroke patients. The thought of this is basic, utilizing the typical EEG estimation that distinguishes the electrical yield of the cerebrum and utilizing them as a data to different gadgets As no persons contemplations are same; this framework gives distinction to the one utilizing it. Deadened patients who can't utilize their appendages and persons experiencing 'secured disorder' whose cerebrum action are all the same can connect utilizing this kind of non intrusive BCI. This paper is a tricky study that considers in the domain of this advancement distinguishing both its advantages and disadvantages as it prompts another time of savvy advances for what's to come. [1],[ 3],[5]

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