Trends and Applications of Brain Computer Interfaces

2020 ◽  
Vol 15 ◽  
Author(s):  
Drishti Yadav ◽  
Shilpee Yadav ◽  
Karan Veer
Keyword(s):  
Search Strategy ◽  
State Of The Art ◽  
Embryonic Stage ◽  
Brain Computer Interfaces ◽  
Research Papers ◽  
Seamless Integration ◽  
Future Directions ◽  
Comprehensive Review ◽  
Computer Interfaces ◽  
Recent Trends

Abstract:: This article provides a comprehensive review of the recent trends and applications of BCIs. This review also provides future directions towards the acceleration and maturation of BCI technology. Based on a methodical search strategy, major technical databases were searched in quest of research papers of average and outstanding interest. A total of 188 research works were contained within this review due to their suitability and state-of-the-art achievements. This review identifies various eminent applications of BCIs in medical and non-medical domains. The findings of this review reveal the need of further exploration of BCI devices outside the laboratory-based settings for their development and seamless integration. In addition, applications of BCIs, including neuromarketing, neurorehabilitation, and neuroergonomics, require additional investigations for further validation and fruition of BCI technology. Based on this review, it is concluded that BCIs are in their embryonic stage and seek further research and investigation for their maturation.

scholarly journals Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

2020 ◽  
Vol 6 ◽  
Author(s):  
Jinwoo Kim
Keyword(s):  
Information Integration ◽  
Visual Analytics ◽  
Construction Projects ◽  
State Of The Art ◽  
Built Environments ◽  
Knowledge Gaps ◽  
Research Papers ◽  
Future Directions ◽  
Construction Monitoring ◽  
Significant Attention

Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. Despite their encouraging findings, it remains a major challenge to exploit technologies in real construction projects, implying that there are knowledge gaps in practice and theory. To fill such knowledge gaps, this study thoroughly reviews 119 papers on operation-level vision-based construction monitoring, published in mainstream construction informatics journals. Existing research papers can be categorized into three sequential technologies: (1) camera placement for operation-level construction monitoring, (2) single-camera-based construction monitoring and documentation, and (3) multi-camera-based onsite information integration and construction monitoring. For each technology, state-of-the-art algorithms, open challenges, and future directions are discussed.

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

Brain-Computer Interface for Cyberpsychology

2019 ◽  
pp. 102-122
Author(s):  
Yujia Peng
Keyword(s):  
General Population ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Clinical Population ◽  
Human Computer Interface ◽  
Brain Computer Interfaces ◽  
User Experiences ◽  
Future Directions ◽  
Computer Interfaces ◽  
The Future

As a new way of implementing human-computer interface, brain-computer interfaces (BCI) dramatically changed the user experiences and have broad applications in cyber behavior research. This chapter aims to provide an overall picture of the BCI science and its role in cyberpsychology. The chapter starts with an introduction of the concept, components, and the history and development of BCI. It is then followed by an overview of neuroimaging technologies and signals commonly used in BCI. Then, different applications of BCI on both the clinical population and the general population are summarized in connection with cyberpsychology. Specifically, applications include communication, rehabilitation, entertainments, learning, marketing, and authentication. The chapter concludes with the future directions of BCI.

scholarly journals Adaptive Classification on Brain-Computer Interfaces Using Reinforcement Signals

2012 ◽  
Vol 24 (11) ◽  
pp. 2900-2923 ◽  
Author(s):  
A. Llera ◽  
V. Gómez ◽  
H. J. Kappen
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Probabilistic Model ◽  
State Of The Art ◽  
Brain Computer Interfaces ◽  
Adaptive Classification ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Parameter Values

We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.

scholarly journals Microencapsulation for the Therapeutic Delivery of Drugs, Live Mammalian and Bacterial Cells, and Other Biopharmaceutics: Current Status and Future Directions

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Catherine Tomaro-Duchesneau ◽  
Shyamali Saha ◽  
Meenakshi Malhotra ◽  
Imen Kahouli ◽  
Satya Prakash
Keyword(s):  
Mammalian Cells ◽  
State Of The Art ◽  
Current Status ◽  
Therapeutic Effects ◽  
Bacterial Cells ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Comprehensive Review ◽  
Therapeutic Delivery ◽  
Physical Chemical

Microencapsulation is a technology that has shown significant promise in biotherapeutics, and other applications. It has been proven useful in the immobilization of drugs, live mammalian and bacterial cells and other cells, and other biopharmaceutics molecules, as it can provide material structuration, protection of the enclosed product, and controlled release of the encapsulated contents, all of which can ensure efficient and safe therapeutic effects. This paper is a comprehensive review of microencapsulation and its latest developments in the field. It provides a comprehensive overview of the technology and primary goals of microencapsulation and discusses various processes and techniques involved in microencapsulation including physical, chemical, physicochemical, and other methods involved. It also summarizes the state-of-the-art successes of microencapsulation, specifically with regard to the encapsulation of microorganisms, mammalian cells, drugs, and other biopharmaceutics in various diseases. The limitations and future directions of microencapsulation technologies are also discussed.

scholarly journals Temperature Sensing Performance of Microsphere Resonators

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2515 ◽  
Author(s):  
Jibo Yu ◽  
Elfed Lewis ◽  
Gilberto Brambilla ◽  
Pengfei Wang
Keyword(s):  
Thermal Expansion ◽  
State Of The Art ◽  
Liquid Droplet ◽  
Review Article ◽  
Temperature Sensing ◽  
Droplet Temperature ◽  
Future Directions ◽  
Comprehensive Review ◽  
Current State ◽  
Sensing Performance

In recent years, many temperature sensing devices based on microsphere resonators have emerged, attracting an increasing research interest. For the purpose of this review article, microsphere resonators are divided according to their constituting materials, namely silicone, silica, compound glass, and liquid droplet. Temperature monitoring relies mainly on the thermo-optic/thermal expansion of the microspheres and on the fluorescence of the doped ions. This article presents a comprehensive review of the current state of the art of microsphere based temperature sensing and gives an indication of future directions.

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