Paper Info
Title
Hidden Markov Models for Reading Words from the Human Brain
Abstract
Recent work has shown that it is possible to reconstruct perceived stimuli from human brain activity. At the same time, studies have indicated that perception and imagery share the same neural substrate. This could bring cognitive brain computer interfaces (BCIs) that are driven by direct readout of mental images within reach. A desirable feature of such BCIs is that subjects gain the ability to construct arbitrary messages. In this study, we explore whether words can be generated from neural activity patterns that reflect the perception of individual characters. To this end, we developed a graphical model where low-level properties of individual characters are represented via Gaussian mixture models and high-level properties reflecting character co-occurrences are represented via a hidden Markov model. With this work we provide the initial outline of a model that could allow the development of cognitive BCIs driven by direct decoding of internally generated messages.
Year
DOI
Venue
2015
10.1109/PRNI.2015.31
PRNI
Keywords
Field
DocType
fMRI, hidden Markov model, visual cortex, brain computer interface, brain decoding, language model
Maximum-entropy Markov model,Pattern recognition,Neural substrate,Computer science,Brain–computer interface,Speech recognition,Mental image,Artificial intelligence,Graphical model,Hidden Markov model,Mixture model,Language model
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
3
Name
Order
Citations
PageRank
Sanne Schoenmakers1362.70
Tom Heskes21519198.44
Marcel Van Gerven332139.35