Phase-based features for motor imagery brain-computer interfaces
Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject's motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase-locking value (PLV). In this study, we...
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| container_title | 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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| creator | Hamner, B. Leeb, R. Tavella, M. del R Millan, J. |
| description | Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject's motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase-locking value (PLV). In this study, we investigated the performance of various phase-based features, including instantaneous phase difference (IPD) and PLV, for control of a MI BCI. Patterns of phase synchrony differentially appear over the motor cortices and between the primary motor cortex (M1) and supplementary motor area (SMA) during MI. Offline results, along with preliminary online sessions, indicate that IPD serves as a robust control signal for differentiating between MI classes, and that the phase relations between channels are relatively stable over several months. Offline and online trial-level classification accuracies based on IPD ranged from 84% to 99%, whereas the performance for the corresponding amplitude features ranged from 70% to 100%. |
| doi_str_mv | 10.1109/IEMBS.2011.6090712 |
| format | Article |
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| subjects | Bayes Theorem BCI Brain computer interfaces Brain modeling EEG Electroencephalography Feature extraction Humans instantaneous phase difference Man-Machine Systems Motor Cortex - physiology motor imagery Niobium phase synchrony Probability Synchronization Training User-Computer Interface |
| title | Phase-based features for motor imagery brain-computer interfaces |
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