2026-06-03T08:21:26Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/388832026-02-03T11:29:04Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ariza Cervera, Isaac
author
Tardón-García, Lorenzo José
author
Barbancho-Pérez, Ana María
author
Barbancho-Pérez, Isabel
2025-06-05T10:37:25Z
2025-06-05T10:37:25Z
2025-05-28
Ariza, I., Tardón, L. J., Barbancho, A. M., & Barbancho, I. (2025). EEG-based listened-language classification. Expert Systems with Applications, 288, 128276.
0957-4174
https://hdl.handle.net/10630/38883
10.1016/j.eswa.2025.128276
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 to
record electroencephalography (EEG) signals from subjects listening to sentences in five different languages, and
a 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 channels
to characterize these signals, different classification tests were conducted using bidirectional Long Short-Term
Memory (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 to
sentences 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 understand
native 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’s
language comprehension level, and can be used for monitoring the learning curve of a new language or to assess
comprehension 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
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Electroencefalografía
Redes neuronales (Neurobiología)
Memoria
EEG-based listened-language classification
journal article
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EEG-based listened-language classification.pdf