Flores, Christian and Contreras, Marcelo and Macedo, Ichiro and Andreu-Perez, Javier (2024) Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering. DOI https://doi.org/10.1109/TNSRE.2024.3420960
Flores, Christian and Contreras, Marcelo and Macedo, Ichiro and Andreu-Perez, Javier (2024) Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering. DOI https://doi.org/10.1109/TNSRE.2024.3420960
Flores, Christian and Contreras, Marcelo and Macedo, Ichiro and Andreu-Perez, Javier (2024) Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering. DOI https://doi.org/10.1109/TNSRE.2024.3420960
Abstract
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-world diversity. Deep learning models aim to enhance BCI classification accuracy, and transfer learning has been suggested to adapt models to individual neural patterns using a base model trained on others’ data. This approach promises better generalizability and reduced overfitting, yet challenges remain in handling diverse and imbalanced datasets from different equipment, subjects, multiple centres in different countries, and both healthy and patient populations for effective model transfer and tuning. In a setting characterized by maximal heterogeneity, we proposed P300 wave detection in BCIs employing a convolutional neural network fitted with adaptive transfer learning based on Poison Sampling Disk (PDS) called Active Sampling (AS), which flexibly adjusts the transition from source data to the target domain. Our results reported for subject adaptive with 40% of adaptive fine-tuning that the averaged classification accuracy improved by 5.36% and standard deviation reduced by 12.22% using two distinct, internationally replicated datasets. These results outperformed in classification accuracy, computational time, and training efficiency, mainly due to the proposed Active Sampling (AS) method for transfer learning.
Item Type: | Article |
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Uncontrolled Keywords: | Adaptive transfer learning; Poison disk sampling; mini-batch sampling; deep learning; EEG-based BCI; P300; CNN; ecological validity |
Subjects: | Z Bibliography. Library Science. Information Resources > ZZ OA Fund (articles) |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Jul 2024 09:43 |
Last Modified: | 18 Jul 2024 09:45 |
URI: | http://repository.essex.ac.uk/id/eprint/38632 |
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