PostDoc: Adaptive Brain-Motor Mapping

Postdoctoral position: Adaptive Brain-Motor Mapping

Neuroprosthetics is an interdisciplinary field related to neuroscience, bioelectronics and biomedical engineering, which aims to substitute a motor, sensory or cognitive function that might have been damaged as a result of an injury or a disease. One of the challenging issues in motor prosthesis is the large variety of patient situations depending on the type of neurological disorder. To overcome the current limited performance of such systems, a robust biosignal processing and a model-based control taking into account the actual sensorimotor state with biosignal feedback would bring a break-through and allow to progress toward adaptive neuroprosthesis.
Recent advances of Brain-Computer-Interfaces (BCI) have opened a new communication channel for patients, who can transmit their movement intention via brain signals.

  • The functionality and controllability of motor prosthesis can be further improved by taking advantage of computational mapping between EMG (Electromyography), EEG (Electroencephalography), and other modalities of biofeedback information.
      The first objective is to enhance the classi cation algorithm to extract the subject’s motor intention from EEG signal in motor-imagery based BCI. The computational modeling between multichannel EMG and EEG will involve advanced feature extraction, dimension reduction and classifi cation algorithms. Moreover EMG signals of multiple muscles and muscle modeling including skeletal dynamics models will help in obtaining the detailed motion intention of the subject.
      The second objective is to develop a bilateral learning architecture. In BCI, adaptive decoding of EEG signals is desirable because brain signals change over time during the learning of the task. In motor control, it is also known that we change how we use our joints and to nd a way to deal with redundancy problems in articulation. In EMG analysis, the change of motor usage can be captured. Adaptive modeling of EMG allows the evaluation of skill acquisition.
  • By jointly analyzing both the EEG and EMG modi cations, we investigate how EEG signal may change along with actual motor coordination changes. By modeling both of these adaptive features, this framework will try to capture the bilateral learning architecture of both the brain and the motor system.
  • Required skills:
    A strong background in signal processing, control, and machine learning is required.
    Fluency in English, and excellent programming skills (C++ and Matlab) are necessary.To apply for this position, please contact Maureen.Clerc[at] or Mitsuhiro.Hayashibe[at] .