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Journal of Neural Engineering

An online brain–machine interface using decoding of movement direction from the human electrocorticogram

Tomislav Milekovic1,2,3,4,7,8, Jörg Fischer2, Tobias Pistohl1,2, Johanna Ruescher5,6, Andreas Schulze-Bonhage1,6, Ad Aertsen1,5, Jörn Rickert1,2, Tonio Ball1,6,9 and Carsten Mehring1,2,3,4,9

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A brain–machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%–86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.


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

87.85.Ng Biological signal processing

87.85.J- Biomaterials

87.19.L- Neuroscience Movement


Medical physics

Biological physics


Issue 4 (August 2012)

Received 15 February 2012, accepted for publication 22 May 2012

Published 19 June 2012


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