Brain-computer interface research at the Wadsworth Center.

By Jonathan R. Wolpaw, Dennis J. McFarland, Theresa M. Vaughan. IEEE transactions on rehabilitation engineering, 2000

514 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/16b187a157ad1599bf785912ac7974e38198be7a

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Human-Computer Interaction

Citations in the corpus

Electroencephalogram-based control of an electric wheelchair (2005, unknown): “[ 8 ] R. Q. van der Linde, “Actively controlled ballistic walking,” in Proc.”
An improved P300-based brain-computer interface (2005, background): “841878 brain waves [1], [11], [12], [19], [20], [27], [39] or by exploiting natural responses of the brain to external stimuli [6], [13], [15].”
Noninvasive brain-actuated control of a mobile robot by human EEG (2004, unknown): “So far control tasks based on human EEG have been limited to simple exercises such as moving a computer cursor or opening a hand orthosis [6]–[9].”
A BCI-based environmental controller for the motion-disabled. (2003, background): “The command for device control can be generated by self-regulated mu rhythm [2]‐[ 4 ], motor imagery [5], [6], or visual evoked potential (VEP) [7], [8].”
Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, methodology): “The performance of the overall system is measured by the information transfer rate (ITR, [54]) measured in bits per minute (bpm): where p is the accuracy of the subject in making decisions between N targets, e.g., in the feedback explained above, N = 2 and p is the accuracy of hitting the correct…”
Design and implementation of a brain-computer interface with high transfer rates (2002, background): “Typical input signals of BCIs include slow cortical potentials [2], or rhythms recorded over sensorimotor cortex [3]–[5],”
Brain-computer interface technology: a review of the first international meeting. (2000, methodology): “Autoregressive (AR) model parameter estimation is a useful method for describing EEG activity, and can prove valuable for BCI applications (e.g., [ 7 ]‐[9]).”
Brain-computer interfaces for communication and control. (2002, methodology): “Bit rate is shown both as bits/trial (i.e. bits/selection ), and as bits/min when 12 selections are made per min (a rate comparable to that of several current BCIs (e.g. Birbaumer et al., 2000; Donchin et al., 2000; Pfurtscheller et al., 2000a; Wolpaw et al., 2000b )).”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “Later, an approach was introduced relying on voluntary modulation of sensorimotor rhythms (SMR; Neuper et al., 1999; Pfurtscheller and da Silva, 1999; Wolpaw et al., 2000; Krusienski and Wolpaw, 2009), which substantially reduced the required training time (Vaughan et al., 2006).”
BCI2000: a general-purpose brain-computer interface (BCI) system (2004, background): “…or lack any useful muscle control (e.g., due to severe cerebral palsy), a BCI system 0018-9294/04$20.00 © 2004 IEEE could give the ability to answer simple questions quickly, control the environment, perform slow word processing, or even operate a neuroprosthesis or orthosis (see [10]–[12]).”
An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate (2004, methodology): “For trial-based applications, the information transfer rate can be computed according to the formula proposed in [13].”