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• W2047429542 endingPage "025016" @default.
• W2047429542 startingPage "025016" @default.
• W2047429542 abstract "Arm end-point position, end-point velocity, and the intended final location or 'goal' of a reach have all been decoded from cortical signals for use in brain-machine interface (BMI) applications. These different aspects of arm movement can be decoded from the brain and used directly to control the position, velocity, or movement goal of a device. However, these decoded parameters can also be remapped to control different aspects of movement, such as using the decoded position of the hand to control the velocity of a device. People easily learn to use the position of a joystick to control the velocity of an object in a videogame. Similarly, in BMI systems, the position, velocity, or goal of a movement could be decoded from the brain and remapped to control some other aspect of device movement. This study evaluates how easily people make transformations between position, velocity, and reach goal in BMI systems. It also evaluates how different amounts of decoding error impact on device control with and without these transformations. Results suggest some remapping options can significantly improve BMI control. This study provides guidance on what remapping options to use when various amounts of decoding error are present." @default.
• W2047429542 created "2016-06-24" @default.
• W2047429542 creator A5029572250 @default.
• W2047429542 creator A5063186557 @default.
• W2047429542 date "2011-03-24" @default.
• W2047429542 modified "2023-09-23" @default.
• W2047429542 title "Decoding position, velocity, or goal: Does it matter for brain–machine interfaces?" @default.
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• W2047429542 doi "https://doi.org/10.1088/1741-2560/8/2/025016" @default.
• W2047429542 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3140465" @default.
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• W2047429542 hasPublicationYear "2011" @default.
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