Brain-Computer Interface (BCI): Types, Processing Perspectives and Applications

Panoulas, Konstantinos J.; Hadjileontiadis, Leontios; Panas, S.M.

doi:10.1007/978-3-642-13396-1_14

Cited by 31 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Training the user is a time-consuming activity either in guiding the user through the process or in the number of recorded sessions. It takes place either in preliminary phase or in the classifier calibration phase [135]. The user is taught to deal with the system as well as to control hisnher brain feedback signals in the preliminary phase, while in the calibration phase, trained subject's signal has been used to learn the used classifier.…”

Section: Usability Challengesmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

460

247

View full text Add to dashboard Cite

Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

show abstract

Section: Usability Challengesmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

460

247

View full text Add to dashboard Cite

show abstract

“…Emotions can be represented using different general models [ 7 ]. The most used are the discrete model and the dimensional models.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based BCI Emotion Recognition: A Survey

Torres

Hernández-Álvarez

et al. 2020

Sensors

193

View full text Add to dashboard Cite

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

show abstract

“…It is a form of noise. ECoGs are less considered with noise than non-invasive BCI, making interpretation easier [44].…”

Section: Partially Invasivementioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

123

View full text Add to dashboard Cite

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

show abstract

Brain-Computer Interface (BCI): Types, Processing Perspectives and Applications

Cited by 31 publications

References 40 publications

Brain computer interfacing: Applications and challenges

Brain computer interfacing: Applications and challenges

EEG-Based BCI Emotion Recognition: A Survey

Brain-Computer Interface: Advancement and Challenges

Contact Info

Product

Resources

About