Name
Abstract | ||
|---|---|---|
This paper proposes a subject transfer framework for EEG classification. It aims to improve the classification performance when the training set of the target subject (namely user) is small owing to the need to reduce the calibration session. Our framework pursues improvement not only at the feature extraction stage, but also at the classification stage. At the feature extraction stage, we first obtain a candidate filter set for each subject through a previously proposed feature extraction method. Then, we design different criterions to learn two sparse subsets of the candidate filter set, which are called the robust filter bank and adaptive filter bank, respectively. Given robust and adaptive filter banks, at the classification step, we learn classifiers corresponding to these filter banks and employ a two-level ensemble strategy to dynamically and locally combine their outcomes to reach a single decision output. The proposed framework, as validated by experimental results, can achieve positive knowledge transfer for improving the performance of EEG classification. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neucom.2011.10.024 | Neurocomputing |
Keywords | Field | DocType |
feature extraction stage,classification step,robust filter bank,eeg classification,candidate filter set,subject transfer framework,feature extraction method,filter bank,adaptive filter bank,classification stage,classification performance,sparse representation,ensemble learning,transfer learning | Data mining,Computer science,Transfer of learning,Knowledge transfer,Artificial intelligence,Adaptive filter,Ensemble learning,Training set,Pattern recognition,Eeg classification,Sparse approximation,Feature extraction,Machine learning | Journal |
Volume | ISSN | Citations |
82, | 0925-2312 | 29 |
PageRank | References | Authors |
1.24 | 19 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenting Tu | 1 | 85 | 9.48 |
| Shiliang Sun | 2 | 1732 | 115.55 |