Identification of perceived sentences using deep neural networks in EEG

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dc.contributor.authorValle, Carlos
dc.contributor.authorMendez-Orellana, Carolina
dc.contributor.authorHerff, Christian
dc.contributor.authorRodriguez-Fernandez, Maria
dc.date.accessioned2025-01-20T16:04:40Z
dc.date.available2025-01-20T16:04:40Z
dc.date.issued2024
dc.description.abstractObjetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data. Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area. Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension. Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.
dc.fuente.origenWOS
dc.identifier.doi10.1088/1741-2552/ad88a3
dc.identifier.eissn1741-2552
dc.identifier.issn1741-2560
dc.identifier.urihttps://doi.org/10.1088/1741-2552/ad88a3
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/89839
dc.identifier.wosidWOS:001346068100001
dc.issue.numero5
dc.language.isoen
dc.revistaJournal of neural engineering
dc.rightsacceso restringido
dc.subjectbrain computer interfaces
dc.subjectdeep neural networks
dc.subjectEEG
dc.subjectsentence identification
dc.subjectspeech decoding
dc.titleIdentification of perceived sentences using deep neural networks in EEG
dc.typeartículo
dc.volumen21
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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