Electroencephalography Reveals Distinct Brain Patterns During Second Language Acquisition in Arabic and Hindi Speakers

Acquiring a new language induces distinct patterns of brain activity detectable with electroencephalography (EEG). This study analyzed EEG signals during second language learning in Arabic and Hindi speakers. Participants were native Arabic or Hindi speakers attempting to learn the other language. EEG data was recorded while participants vocalized words in each language. Time-frequency analysis showed increased power in the alpha and beta bands for second languages versus native languages. Functional connectivity in the alpha band was also stronger for second languages. A stacking ensemble machine learning model achieved up to 96.62% accuracy in classifying Arabic and Hindi EEG patterns. These results demonstrate that second language acquisition elicits specific neurolinguistic biomarkers in EEG recordings. Studying typologically different languages provides insights into both universal and language-dependent learning effects. The observed changes likely reflect recruitment of expanded brain networks as proficiency develops. Further research can build on these findings to track acquisition timelines, localize involved cortical regions, and optimize second language training. This study highlights the utility of EEG and machine learning for investigating neural processes underlying second language mastery.