Conference
Visual cues can bias EEG Deep Learning models
| العنوان: | Visual cues can bias EEG Deep Learning models |
|---|---|
| المؤلفون: | Trocellier, David, N'Kaoua, Bernard, Lotte, Fabien |
| المساهمون: | Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB), Fabien lotte, Camille Jeunet-Kelway, Frontiers, ANR-22-CE33-0015,PROTEUS,Proteus: mesurer, comprendre et combattre les variabilités dans les interactions cerveau-ordinateur(2022) |
| المصدر: | Neuroergonomics 2024 - The 5th International Conference ; https://hal.science/hal-04651337 ; Neuroergonomics 2024 - The 5th International Conference, Fabien lotte; Camille Jeunet-Kelway, Jul 2024, Bordeaux, France |
| بيانات النشر: | CCSD |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Artificial Intelligence AI, Salency map, MI-BCI, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science |
| جغرافية الموضوع: | Bordeaux, France |
| الوصف: | International audience ; The use of Deep Learning (DL) for classifying motor imagery-based brain-computer interfaces (MI-BCIs) has seen significant growth over the past years, promising to enhance EEG classification accuracies. However, the black-box nature of DL may lead to accurate but biased and/or irrelevant DL models. Here, we study the influence of using visual cue EEG (which is commonly done) in the DL input window on both the features learned and the classification performance of a state-of-the-art DL model, DeepConvNet. The classifier was tested on a large MI-BCI dataset with two time windows post visual cue: 0-4s (with the cue EEG) and 0.5-4.5s (without). Performance-wise, the first condition significantly outperformed the second (86.82% vs. 76.11%, p<0.001). However, saliency maps analyses demonstrated that the inclusion of the visual cue EEG leads to the extraction of cue-related evoked potentials, which are distinct from the MI features used by the model trained without visual cues EEG. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| الاتاحة: | https://hal.science/hal-04651337 https://hal.science/hal-04651337v1/document https://hal.science/hal-04651337v1/file/Neuroergonomics_2024_DT.pdf |
| Rights: | http://hal.archives-ouvertes.fr/licences/publicDomain/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.6B6716D0 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |