Rapid prototyping of an EEG-based brain-computer interface (BCI)

By Christoph Guger, Alois Schlögl, Christa Neuper, D. Walterspacher, Thomas Strein, Gert Pfurtscheller. IEEE transactions on neural systems and rehabilitation engineering, 2001

344 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/34ec1ca8faac3bf1fc21ed1949bd814f02deb46b

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, background): “The electrical activity can be measured by electroencephalogram (EEG) [1,2].”
BCI2000: a general-purpose brain-computer interface (BCI) system (2004, background): “While a few systems have attempted to solve this problem (e.g., [14]–[16]), BCI research up to the present has consisted mainly of demonstrations that a certain brain signal recorded and measured in a certain way, and translated into control commands by a certain algorithm, can control a certain…”
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, methodology): “EEG and ECog have currently found an application as a brain-machine interface [102], and in theory, can control the movement of the prosthesis similar to the EMG.”
Better than random? A closer look on BCI results (2008, background): “• In Guger et al. [2001] a 2-class BCI (80 trials/class) training was performed.”
An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate (2004, background): “3) Noncue-based (asynchronous) information transfer: The real-time Graz-BCI operates in a cue-based or synchronous communication mode [4], [7].”
The functional significance of mu rhythms: translating "seeing" and "hearing" into "doing". (2005, background): “[50] reported a similar type of rapid training.”
How many people are able to control a P300-based brain-computer interface (BCI)? (2009, neutral): “Thus, offline (after the calibration data have been collected) single trial epochs of 800 ms are used to derive the LDA weighting coefficients.”
How many people are able to operate an EEG-based brain-computer interface (BCI)? (2003, methodology): “Feedback plays an essential role in BCI skill development as indicated by several investigations [6], [8], [11], [15].”