RT Journal Article
T1 EEG-based listened-language classification
A1 Ariza Cervera, Isaac
A1 Tardón-García, Lorenzo José
A1 Barbancho-Pérez, Ana María
A1 Barbancho-Pérez, Isabel
K1 Electroencefalografía
K1 Redes neuronales (Neurobiología)
K1 Memoria
AB From an early age, individuals are continuously exposed to other languages beyond their native tongue; however,the brain’s response to these auditory stimuli remains unclear. To investigate this, an experiment was designed torecord electroencephalography (EEG) signals from subjects listening to sentences in five different languages, anda specific database was built to enable performing classification tests to distinguish between different languages,and varying levels of language comprehension. By analysing the energy difference between the EEG channelsto characterize these signals, different classification tests were conducted using bidirectional Long Short-TermMemory (bi-LSTM), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks.The main objective is the analysis of the brain’s response in two different scenarios: when the subject listens tosentences in different languages, and when the subject understands or misunderstands the meaning of a sentence.In the multi-class classification involving sentences in five different languages, the accuracy attained is 36.37 %.However, in the multi-class classification between ‘understood’/‘understood part of the meaning’/‘didn’t under stand’, the accuracy attained reaches 81.36 %. The results obtained for binary classification tests of understandnative language or foreign language is 89.09 %. The bi-LSTM neural network achieved the overall best perfor mance.These results demonstrate that the analysis of the EEG signals alone can give information regarding a person’slanguage comprehension level, and can be used for monitoring the learning curve of a new language or to assesscomprehension in patients with conditions such as aphasia.The authors are grateful to María José Varela  (Professor at the Department of Translation and Interpretation, University of Málaga) for her valuable assistance with the samples used to build this database
PB Elsevier
SN 0957-4174
YR 2025
FD 2025-05-28
LK https://hdl.handle.net/10630/38883
UL https://hdl.handle.net/10630/38883
LA eng
NO Ariza, I., Tardón, L. J., Barbancho, A. M., & Barbancho, I. (2025). EEG-based listened-language classification. Expert Systems with Applications, 288, 128276.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026