Jeremy Hill
homepage:http://www.kyb.mpg.de/~jez
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Description

His principal interest is in applying machine-learning methods to the development of brain-computer interfaces (BCI). This involves the classification of a user's intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced amyotrophic lateral sclerosis) could use to communicate. He is currently pursuing some of these questions in collaboration with Dr. Jason Farquhar, Prof. Peter Desain and others at the Donders Centre for Brain, Cognition and Behaviour in Nijmegen.


Lectures:

lecture
flag Machine Learning for Brain-Computer Interfaces
as author at  Mini Symposia,
991 views
  lecture
flag BCPy2000
as author at  NIPS Workshop on Machine Learning Open Source Software, Whistler 2008,
818 views
lecture
flag New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams
as author at  Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, Berlin 2007,
590 views