Using a Data-Driven Approach to Estimate Second-Language Proficiency From Brain Activation: A Functional...

Area of Science:

• Neuroscience
• Cognitive Science
• Brain Imaging

Background:

• Non-invasive brain imaging advances human brain science.
• Estimating individual brain states is crucial for societal applications.
• Brain activation patterns can reflect cognitive states like language proficiency.

Purpose of the Study:

• To investigate the use of functional near-infrared spectroscopy (fNIRS) for classifying second-language proficiency.
• To identify brain activation patterns associated with varying levels of language ability.
• To explore the societal applications of fNIRS in understanding individual cognitive states.

Main Methods:

• Utilized functional near-infrared spectroscopy (fNIRS) to measure cortical activity.
• Recruited native Japanese and English speakers with varying proficiency levels in the other language.
• Employed a data-driven approach to analyze brain activation during listening comprehension tasks.

Main Results:

• Achieved high classification accuracy for second-language proficiency: 77.5% for Japanese speakers and 81.9% for English speakers.
• Identified informative brain features predominantly from language-associated regions.
• Demonstrated the potential of fNIRS in differentiating language abilities based on neural activity.

Conclusions:

• fNIRS is a viable tool for assessing second-language proficiency through brain activation patterns.
• The study provides valuable insights into fNIRS neuroscience and its practical applications.
• Neural correlates of language function can be effectively detected using fNIRS.