Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain–Computer Interface

Ron-Angevín, Ricardo; Fernández-Rodríguez, Álvaro; Dupont, Clara; Maigrot, Jeanne; Meunier, Juliette; Tavard, Hugo; Lespinet‐Najib, Véronique; André, Jean‐Marc

doi:10.3390/s23031304

Cited by 3 publications

(2 citation statements)

References 29 publications

(55 reference statements)

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“…Other studies have looked into how BCI technology can be used for more complex forms of communication, such as typing on a computer keyboard or giving speech commands via voice recognition software [131][132][133][134][135][136]. These types of applications could prove useful for helping individuals with severe motor impairments regain some level of independence when communicating with others.…”

Section: Communicationmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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Section: Communicationmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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“…Raw data in BCI tasks is often highly noisy, especially for noninvasive approaches, because the underlying signal arises within the central nervous system, peripheral nervous system, or muscles, and is attenuated before reaching sensors [69]- [71]. Even within very specific BCI applications, such as EEG-based communication, there are often important design choices, such as the number of choices presented to a user during each trial, the duration of a trial, or the delay between trials, so that the set of labels being used and structure of data epochs may not be compatible across experiments [40], [72], [73].…”

Section: Sources Of Noisementioning

confidence: 99%

Reducing calibration effort for brain-computer interfaces

Smedemark-Margulies

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This thesis represents the culmination of my PhD journey. To begin, I want to thank my parents, siblings, extended family, and most of all, my wife Krista, for endless support and encouragement through this marathon process. I feel extremely grateful to have such wonderful people in my life.During my PhD, I have had the great privilege of working with a large set of talented researchers across several institutions. As you may know, conducting an extended academic research project involves many challenges. Timelines are often tight, experiments that 'should' succeed often fail, and Reviewer #2 usually hasn't had enough coffee. Despite these obstacles, I have been fortunate to benefit from and learn from the patience, enthusiasm, and creative ideas of my mentors and co-authors.First, I want to thank my advisors Deniz Erdo gmus ¸, Robin Walters, and Jan-Willem van de Meent. Without their support, none of the work in this thesis would have been possible. This includes mundane elements like funding and organization, and more exciting elements such as iterating on research questions and experiments. I have learned a tremendous amount from each of them, and having the benefit of advice and mentorship from three fantastic researchers with different backgrounds and expertise has been an important advantage for me.Next, I want to thank my co-authors 1 from research described in this thesis and from other projects. People are listed here according to their affiliation when we worked together.

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User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface

Dillen,

Omidi,

Ghaffari

et al. 2024

Sensors

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This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

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Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain–Computer Interface

Cited by 3 publications

References 29 publications

State-of-the-Art on Brain-Computer Interface Technology

State-of-the-Art on Brain-Computer Interface Technology

Reducing calibration effort for brain-computer interfaces

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface

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