Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Review
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
The Extreme Energy Ratio Criterion for EEG Feature Extraction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
IEEE Transactions on Knowledge and Data Engineering
Neural Computing and Applications
Spatial filter selection with LASSO for EEG classification
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Classifying event-related desynchronization in EEG, ECoG and MEG signals
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
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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.