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

@inproceedings{Wang2018AJILEMP,
  title={AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video},
  author={Xin Ru Nancy Wang and Ali Farhadi and Rajesh P. N. Rao and Bingni W. Brunton},
  booktitle={AAAI},
  year={2018}
}
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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Neural correlates to automatic behavior estimations from RGB-D video in epilepsy unit

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) • 2016
View 1 Excerpt

Flowing ConvNets for Human Pose Estimation in Videos

2015 IEEE International Conference on Computer Vision (ICCV) • 2015
View 2 Excerpts

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