News Release

Machine learning classifies word type based on brain activity

Technique could be used to develop task-free language impairment diagnosis

Peer-Reviewed Publication

Society for Neuroscience

Machine Learning Classifies Word Type Based on Brain Activity

image: This is an activity comparison for real (left) and pseudo (right) words. view more 

Credit: Jensen et al., <i>eNeuro</i> 2019

Pairing machine learning with neuroimaging can determine whether a person heard a real or made up word based on their brain activity, according to a new study published in eNeuro. These results lay the groundwork for investigating language processing in the brain and developing an imaging-based tool to assess language impairments.

Many brain injuries and disorders cause language impairments that are difficult to establish with standard language tasks because the patient is unresponsive or uncooperative, creating a need for a task-free diagnosis method. Using magnetoencephalography, Mads Jensen, Rasha Hyder, and Yury Shtyrov from Aarhus University examined the brain activity of participants while they listened to audio recordings of both similar-sounding real words with different meanings and made up "pseudowords." The participants were then instructed to ignore the words and focus on a silent film.

Using machine learning algorithms, the team was able to determine when a participant was hearing a real or made up word, a grammatically correct or incorrect word, and the word's meaning based on their brain activity. They also identified specific brain regions and frequencies responsible for processing different types of language.


Manuscript title: MVPA Analysis of Intertrial Phase Coherence of Neuromagnetic Responses to Words Reliably Classifies Multiple Levels of Language Processing in the Brain

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About eNeuro

eNeuro, the Society for Neuroscience's open-access journal launched in 2014, publishes rigorous neuroscience research with double-blind peer review that masks the identity of both the authors and reviewers, minimizing the potential for implicit biases. eNeuro is distinguished by a broader scope and balanced perspective achieved by publishing negative results, failure to replicate or replication studies. New research, computational neuroscience, theories and methods are also published.

About The Society for Neuroscience

The Society for Neuroscience is the world's largest organization of scientists and physicians devoted to understanding the brain and nervous system. The nonprofit organization, founded in 1969, now has nearly 37,000 members in more than 90 countries and over 130 chapters worldwide.

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