Name
Abstract | ||
|---|---|---|
We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2% for the LDA and MLP features respectively. We also explore portability of these features across different subjects. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610411 | EMBC |
Keywords | Field | DocType |
discriminative feature extractors,nonlinear classifier,eeg classification,bioelectric potentials,linear discriminant analysis projection output,standard autoregressive features,discriminative tandem features,mlp class-posterior probability,electroencephalography,two-class motor-imagery classification task,lda projection output,medical signal processing,artificial neural network-hidden markov models,multilayer perceptrons,brain-computer-interface (bci),autoregressive processes,tandem configuration,multilayer perceptron class-posterior probability,complementary input features,signal classification,conventional hmm system,hmm-based eeg classification system,hidden markov models,probability,principal component analysis,feature extraction | Autoregressive model,Pattern recognition,Computer science,Linear model,Markov chain,Speech recognition,Multilayer perceptron,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Hidden Markov model,Discriminative model | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chee-Ming Ting | 1 | 72 | 13.17 |
| Simon King | 2 | 1438 | 114.49 |
| S. Hussain | 3 | 47 | 9.46 |
| A K Ariff | 4 | 0 | 0.34 |