Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI
| Autor: | Simon R. H. Davies, Christopher J. James |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Signal processing
Imagery Psychotherapy Computer science business.industry Speech recognition Electroencephalography Signal Processing Computer-Assisted Pattern recognition Hilbert–Huang transform Spatio-Temporal Analysis Motor imagery Brain-Computer Interfaces Humans Sensitivity (control systems) Artificial intelligence business Algorithms Brain–computer interface |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc.2014.6944656 |
| Popis: | This paper introduces a new signal processing method called Spatio-Temporal Multivariate Empirical Mode Decomposition (ST-MEMD). It is a new variation of Empirical Mode Decomposition (EMD) that takes spatial and temporal information into account simultaneously rather than processing each signal source in isolation. The original and new methods were tested on single-trial electroencephalogram data with a two-class problem, in this case data using the Motor Imagery paradigm in brain-computer interfacing. However, whilst ST-MEMD retained the increase in sensitivity and specificity from adding spatial data, the new temporal data made no meaningful difference in terms of performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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