Patents by Inventor Marie-Caroline SCHAEFFER

Marie-Caroline SCHAEFFER has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Continuous decoding direct neural interface which uses a markov mixture of experts
    Patent number: 10832122
    Abstract: A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.
    Type: Grant
    Filed: June 6, 2017
    Date of Patent: November 10, 2020
    Assignee: COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
    Inventors: Marie-Caroline Schaeffer, Tetiana Aksenova
  • CONTINUOUS DECODING DIRECT NEURAL INTERFACE WHICH USES A MARKOV MIXTURE OF EXPERTS
    Publication number: 20180005105
    Abstract: A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.
    Type: Application
    Filed: June 6, 2017
    Publication date: January 4, 2018
    Applicant: COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
    Inventors: Marie-Caroline SCHAEFFER, Tetiana AKSENOVA