Summary: Selecting the signals for a brain­machine interface
Richard A Andersen, Sam Musallam and Bijan Pesaran
Brain­machine interfaces are being developed to assist
paralyzed patients by enabling them to operate machines
with recordings of their own neural activity. Recent studies
show that motor parameters, such as hand trajectory, and
cognitive parameters, such as the goal and predicted value of
an action, can be decoded from the recorded activity to
provide control signals. Neural prosthetics that use
simultaneously a variety of cognitive and motor signals can
maximize the ability of patients to communicate and interact
with the outside world. Although most studies have recorded
electroencephalograms or spike activity, recent research
shows that local field potentials (LFPs) offer a promising
additional signal. The decode performances of LFPs and
spike signals are comparable and, because LFP recordings
are more long lasting, they might help to increase the lifetime
of the prosthetics.
Addresses
Division of Biology, Mail Code 216-76, California Institute of Technology,

Source: Andersen, Richard - Division of Biology, California Institute of Technology

Collections: Biology and Medicine