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Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

  1. Tonio Ball1,5
  1. 1Epilepsy Center, University Medical Center Freiburg, 79106 Freiburg, Germany
  2. 2Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
  3. 3Department of Paediatric Neurology, 2nd Faculty of Medicine and Motol University Hospital
  4. 4Department of Neurology, 2nd Faculty of Medicine and Motol University Hospital, Charles University, 150 06 Prague, Czech Republic
  5. 5Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
  6. 6CorTec GmbH, 79110 Freiburg, Germany
  1. Address correspondence to Jiří Hammer. Email: hammer{at}


How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals.

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