Switch to: Citations

Add references

You must login to add references.
  1. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design.Fabien Lotte, Florian Larrue & Christian Mühl - 2013 - Frontiers in Human Neuroscience 7.
    Download  
     
    Export citation  
     
    Bookmark   20 citations  
  • Critiquing the Concept of BCI Illiteracy.Margaret C. Thompson - 2019 - Science and Engineering Ethics 25 (4):1217-1233.
    Brain–computer interfaces are a form of technology that read a user’s neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as “BCI illiteracy,” and a body of research has emerged aiming to characterize, predict, and (...)
    Download  
     
    Export citation  
     
    Bookmark   10 citations  
  • Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling.Minji Lee, Jae-Geun Yoon & Seong-Whan Lee - 2020 - Frontiers in Human Neuroscience 14.
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI.Jianjun Meng & Bin He - 2019 - Frontiers in Human Neuroscience 13.
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface.Minkyu Ahn, Hohyun Cho, Sangtae Ahn & Sung C. Jun - 2018 - Frontiers in Human Neuroscience 12.
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment.Filip Škola, Simona Tinková & Fotis Liarokapis - 2019 - Frontiers in Human Neuroscience 13:460265.
    This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. Aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing the body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations