Gernot Müller-Putz; G. Pfurtscheller · 2007 · IEEE Transactions on Biomedical Engineering
Paper
Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.
Analysis
This paper presents an asynchronous, four-class BCI using steady-state visual evoked potentials (SSVEPs) to control a two-axes electrical hand prosthesis for restoring grasp function.
Discovery
Moein Khajehnejad; Forough Habibollahi; Tommaso Boccato; Margarida Sousa; Michal Olak; Francesco Jamal Sheiban; Matteo Ferrante
Canyang Zhao; Bolin Peng; J. Patrick Mayo; Ce Ju; Bing Liu
Ben Tang; Zachary Spalding; Gregory B. Cogan
Jintao Zhang; Yidan Ding; Joshua Kosnoff; Maxim Karrenbach; Hanwen Wang; Bin He
Khaleelulla Khan Nazeer; Sirine Arfa; Matthias Jobst; Richard George; Christian Mayr
Sungho Kim; Sarah Kim
Source record