Abstract
While non-invasive brain imaging has made substantial contributions to advance human brain science, estimation of individual state is becoming important to realize its applications in society. Brain activations were used to classify second-language proficiencies. Participants in functional near-infrared spectroscopy (fNIRS) experiment were 20/20 native Japanese speakers with high/low English abilities and 19/19 native English speakers with high/low Japanese abilities. Their cortical activities were measured by functional near-infrared spectroscopy while they were conducting Japanese/English listening comprehension tests. The data-driven method achieved classification accuracy of 77.5% in the case of Japanese speakers and 81.9% in the case of English speakers. The informative features predominantly originated from regions associated with language function. These results bring an insight of fNIRS neuroscience and its applications in society.
Highlights
Language, which differentiates human beings from other living species, plays an important role in our daily lives
All the algorithms showed higher classification accuracy for L2. These results suggest that the informative features, which are related to second-language proficiency, are important for improving classification accuracy
Machine-learning methods were used for distinguishing second-language proficiency individually for both native Japanese speakers and native English speakers
Summary
Language, which differentiates human beings from other living species, plays an important role in our daily lives. Since conventional analysis of fNIRS data has focused on human brain activity at the group level, these studies have traditionally drawn a population-level conclusion about general patterns across a large number of participants. Knowledge from these studies has important implications for advancing our understanding of how the human brain processes language. To further translate this knowledge into practical applications in society, individual estimation or classification of language ability (e.g., speech-comprehension level and second-language proficiency) on the basis of neuroimaging data across participants is a topic of interest
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