Brain-Computer Interface review

Bularka, Szilard; Gontean, Aurel

doi:10.1109/isetc.2016.7781096

Cited by 26 publications

(17 citation statements)

References 10 publications

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“…The commercialization of BCIs would also require the establishment of industry standards, particularly in terms of databases used for benchmarking, EEG recoding equipment and software applications [177]. International roadmap projects such as BNCI Horizon 2020 [178] have attempted to improve communication within industry and research, so that such issues can be resolved.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…BCI illiteracy is another impediment to the universal adoption of BCI technologies, particularly in EEG-based interfaces. Illiteracy occurs when a user is unable to control a BCI because they do not manage to produce the high-quality brain signals required [177,187]. EEG signal quality, as well as overall mastery of the BCI, can be improved by using an interactive, co-learning approach which provides the user with feedback, such as audio or visual feedback, as they use the system [177].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Long-term use of BCIs requires repeated use of particular neural pathways, and more research needs to be carried out in order to identify the possible health implications or changes in brain functionality due to this. For example, studies have indicated that long-term use of external actuators through BCIs has led to restructuring of the brain’s map of the body, with the actuators being perceived by the brain as an extension to the subject’s body [177,188].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Such ethics would establish liability in the case of accidents occurring during the use of controlled apparatus, as well as deal with the appropriate use of bio-signal data and privacy. The BCI Society aims to meet some of these needs by releasing standards and guidelines associated with ethical issues [8,177,178].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

See 3 more Smart Citations

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Padfield

Zabalza

Zhao

et al. 2019

Sensors

368

238

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Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Padfield

Zabalza

Zhao

et al. 2019

Sensors

368

238

View full text Add to dashboard Cite

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“…broad topic. Furthermore, this review does not cover Brain Computer Interfaces (BCIs), first as they constitute an independent topic itself at the interface between engineering and neuroscience (Bulárka and Gontean 2016). Secondly, the vast majority of BCI works relies on a statistical model of the neural signal, often combined with machine learning approaches, and thus lie outside the scope of this review.…”

Section: Introductionmentioning

confidence: 99%

Computational Models in Electroencephalography

et al. 2021

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Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

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Investigations on Motor Imagery in Brain–Computer Interface

Dhiman

Pawan

2022

Lecture Notes in Electrical Engineering

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Brain-Computer Interface review

Cited by 26 publications

References 10 publications

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Computational Models in Electroencephalography

Investigations on Motor Imagery in Brain–Computer Interface

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