Non-supervised Feature Selection: Evaluation in a BCI for Single-Trial Recognition of Gait Preparation/Stop
- 94 Downloads
Is presented a non-supervised method for feature selection based on similarity index, which is applied in a brain-computer interface (BCI) to recognize gait preparation/stops. Maximal information compression index is here used to obtain redundancies, while representation entropy value is employed to find the feature vectors with high entropy. EEG signals of six subjects were acquired on the primary cortex during walking, in order to evaluate this approach in a BCI. The maximum accuracy was 55 and 85 % to recognize gait preparation/stops, respectively. Thus, this method can be used in a BCI to improve the time delay during dimensionality reduction.
KeywordsPredictive Positive Value Dimensionality Reduction Motor Imagery True Positive Rate Band Power
Authors would like to thank CNPq, CAPES, FAPES and SENESCYT for supporting this research.
- 1.B. Graimann, J.E. Huggins, S.P. Levine, G. Pfurtscheller, Towards a direct brain interface based on human subdural recording and wavelet-packet analysis. IEEE Trans. Biomed. Eng. 51(6) (2004)Google Scholar
- 2.P. Mitra, C.A. Murthy, S.K. Pal, Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 1–13 (2002)Google Scholar