Corpus ID: 4554201

AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

  title={AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video},
  author={X. Wang and Ali Farhadi and R. Rao and Bingni W. Brunton},
  • X. Wang, Ali Farhadi, +1 author Bingni W. Brunton
  • Published in AAAI 2018
  • Computer Science, Biology
  • Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that… CONTINUE READING
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    Publications referenced by this paper.
    Flowing ConvNets for Human Pose Estimation in Videos
    • 359
    • PDF
    Deep learning with convolutional neural networks for EEG decoding and visualization
    • 476
    • PDF
    Multimodal Deep Learning
    • 1,942
    • PDF
    Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex
    • 64
    • PDF
    DeepPose: Human Pose Estimation via Deep Neural Networks
    • 1,418
    • PDF
    Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
    • 1,549
    • PDF
    Using the electrocorticographic speech network to control a brain-computer interface in humans.
    • 146
    • PDF
    Neural correlates to automatic behavior estimations from RGB-D video in epilepsy unit
    • 6