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Error-related electrocorticographic activity in humans during continuous movements


Tomislav Milekovic1,2,3, Tonio Ball1,4, Andreas Schulze-Bonhage1,4, Ad Aertsen1,5 and Carsten Mehring1,2,3

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Brain–machine interface (BMI) devices make errors in decoding. Detecting these errors online from neuronal activity can improve BMI performance by modifying the decoding algorithm and by correcting the errors made. Here, we study the neuronal correlates of two different types of errors which can both be employed in BMI: (i) the execution error, due to inaccurate decoding of the subjects' movement intention; (ii) the outcome error, due to not achieving the goal of the movement. We demonstrate that, in electrocorticographic (ECoG) recordings from the surface of the human brain, strong error-related neural responses (ERNRs) for both types of errors can be observed. ERNRs were present in the low and high frequency components of the ECoG signals, with both signal components carrying partially independent information. Moreover, the observed ERNRs can be used to discriminate between error types, with high accuracy (≥83%) obtained already from single electrode signals. We found ERNRs in multiple cortical areas, including motor and somatosensory cortex. As the motor cortex is the primary target area for recording control signals for a BMI, an adaptive motor BMI utilizing these error signals may not require additional electrode implants in other brain areas.


87.19.L- Neuroscience

87.85.J- Biomaterials

87.85.Ng Biological signal processing

87.19.R- Mechanical and electrical properties of tissues and organs

87.19.rs Movement


Medical physics

Biological physics


Issue 2 (April 2012)

Received 24 October 2011, accepted for publication 12 December 2011

Published 13 February 2012

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