NDLI: A reductionist approach to the analysis of learning in brain

Content Provider	Springer Nature Link
Author	Danziger, Zachary
Copyright Year	2014
Abstract	The complexity and scale of brain–computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.
Starting Page	183
Ending Page	201
Page Count	19
File Format	PDF
ISSN	03401200
Journal	Biological Cybernetics
Volume Number	108
Issue Number	2
e-ISSN	14320770
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-02-15
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Brain–computer interface Machine learning Motor learning Cyberglove Neurosciences Computer Application in Life Sciences Neurobiology Bioinformatics Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Computer Science Biotechnology

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