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Título : Multiple Instance Learning Selecting Time-Frequency Features for Brain Computing Interfaces
Autor : Caicedo Acosta, Julian
Velasquez Martinez, Luisa
Cárdenas Peña, David
Castellanos Dominguez, Germán
Palabras clave : ELECTROENCEPHALOGRAFY;MOTOR IMAGERY;MULTI - INSTANCE LEARNING;FEATURE SELECTION;INSTANCE SELECTION
Fecha de publicación : 2018
Editorial : Springer
Citación : Caicedo-Acosta J., Velasquez-Martinez L., Cárdenas-Peña D., Castellanos-Dominguez G. (2018) Multiple Instance Learning Selecting Time-Frequency Features for Brain Computing Interfaces. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_37
Resumen : Brain-Computer Interface is a technology which uses measures of brain activity to help people with motor disabilities. BCI applications based on Electroencephalography commonly rely on Motor Imagery paradigm. However, the estimation of motor brain patterns is affected by both variations in the signal properties over time (i.e. non-stationarity) and differences between frequency bands activations. Generally, Common Spatial Patterns is used as feature extraction. Nevertheless, its performance depends on the filter band selection and the time when the brain activity is associated with the task. A new method of time-frequency segmentation based on multi-instance learning is proposed. The spatial filters are built taking to account the obtained frequency-temporal segments where an instance selection based on Sparse Representation Classification method is developed together with a feature selection stage. The experiments are developed using a well-known dataset BCI competition IV dataset IIa that contains EEG records of nine subjects recorded from 22-electrodes mesh. The results evidencing that significant features appear at the end of MI interval and the found spatial patterns are consistent with MI neurophysiology. Furthermore, the proposed method outperforms the average classification accuracy of both CSP and SFTOFCRC for 8.21% and 1.23% respectively without deteriorating classification accuracy with statistical significance for subjects that present high accuracy with the compared methods.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9483
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