Adaptive deep feature representation learning for cross-subject EEG decoding
Abstract Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficien...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-06024-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559065195446272 |
|---|---|
| author | Shuang Liang Linzhe Li Wei Zu Wei Feng Wenlong Hang |
| author_facet | Shuang Liang Linzhe Li Wei Zu Wei Feng Wenlong Hang |
| author_sort | Shuang Liang |
| collection | DOAJ |
| description | Abstract Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification. Methods: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects. Results: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets. Conclusions: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications. |
| format | Article |
| id | doaj-art-5aeaa8741e32431fa2f81a881785be8c |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-5aeaa8741e32431fa2f81a881785be8c2025-01-05T12:48:11ZengBMCBMC Bioinformatics1471-21052024-12-0125111910.1186/s12859-024-06024-wAdaptive deep feature representation learning for cross-subject EEG decodingShuang Liang0Linzhe Li1Wei Zu2Wei Feng3Wenlong Hang4School of Internet of Things, Nanjing University of Posts and TelecommunicationsSchool of Chemistry and Life Sciences, Nanjing University of Posts and TelecommunicationsSchool of Chemistry and Life Sciences, Nanjing University of Posts and TelecommunicationsDepartment of Electrical and Computer Systems Engineering, Monash UniversityCollege of Computer and Information Engineering/College of Artificial Intelligence, Nanjing Tech UniversityAbstract Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification. Methods: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects. Results: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets. Conclusions: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications.https://doi.org/10.1186/s12859-024-06024-wElectroencephalogramDomain adaptationDiscriminative feature learningEntropy minimizationMotor imagery |
| spellingShingle | Shuang Liang Linzhe Li Wei Zu Wei Feng Wenlong Hang Adaptive deep feature representation learning for cross-subject EEG decoding BMC Bioinformatics Electroencephalogram Domain adaptation Discriminative feature learning Entropy minimization Motor imagery |
| title | Adaptive deep feature representation learning for cross-subject EEG decoding |
| title_full | Adaptive deep feature representation learning for cross-subject EEG decoding |
| title_fullStr | Adaptive deep feature representation learning for cross-subject EEG decoding |
| title_full_unstemmed | Adaptive deep feature representation learning for cross-subject EEG decoding |
| title_short | Adaptive deep feature representation learning for cross-subject EEG decoding |
| title_sort | adaptive deep feature representation learning for cross subject eeg decoding |
| topic | Electroencephalogram Domain adaptation Discriminative feature learning Entropy minimization Motor imagery |
| url | https://doi.org/10.1186/s12859-024-06024-w |
| work_keys_str_mv | AT shuangliang adaptivedeepfeaturerepresentationlearningforcrosssubjecteegdecoding AT linzheli adaptivedeepfeaturerepresentationlearningforcrosssubjecteegdecoding AT weizu adaptivedeepfeaturerepresentationlearningforcrosssubjecteegdecoding AT weifeng adaptivedeepfeaturerepresentationlearningforcrosssubjecteegdecoding AT wenlonghang adaptivedeepfeaturerepresentationlearningforcrosssubjecteegdecoding |