Efecto de la Cantidad y Dimensión de los Patrones en una Interfaz Cerebro Computadora Basada en Discriminante Lineal de Fisher

摘要

The brain-computer interfaces (BCI) translate brain activity into commands for a computer. To improve the performance of BCI, it is necessary to improve the feature extraction techniques that are used to decode the intentions of the users and get a clear understanding of the basic conditions for training the classifier. In this paper we study the behavior of a linear discriminant analysis by varying the number of patterns required for training, and the number of elements used to form patterns. From the results we can conclude that for this application BCI obtain optimal performance when used about eight training patterns for each feature used. In addition, a subsample of 8 Hz value of the temporal signais of the rows of EEG showed the best overall performance as a feature extraction technique.