Innovative machine learning method anticipates neurocognitive
changes, similar to predictive text entry for cell phones,
Internet search engines At UCLA’s Laboratory of Integrative
Neuroimaging Technology, researchers use functional MRI brain
scans to observe brain signal changes that take place during
mental activity. They then employ computerised machine learning
(ML) methods to study these patterns and identify the cognitive
state – or sometimes the thought process – of human subjects.
The technique is called “brain reading” or “brain decoding.” In
a new study, the UCLA research team describes several crucial
advances in this field, using fMRI and machine learning methods
to perform “brain reading” on smokers experiencing nicotine
cravings. The research, presented last week at the Neural
Information Processing Systems’ Machine Learning and
Interpretation in Neuroimaging workshop in Spain, was funded by
the National Institute on Drug Abuse, which is interested in
using these method to help people control dr