A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods

A Local Neural Classifier for the Recognition of EEG Patterns Associated to Mental Tasks

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneousEEGsignals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former

Retroalimentación en el entrenamiento de una interfaz cerebro computadora usando técnicas basadas en realidad virtual

Una interfaz cerebro-computadora o BCI (Brain-Computer Interface), se basa principalmente en el análisis de las señales electroencefalográficas (señales EEG) captadas durante algún tipo de actividad mental con la finalidad de controlar un componente externo. Sus prestaciones dependerán en gran medida de la capacidad que tenga un sujeto para controlar sus patrones EEG, siendo necesario un adecuado entrenamiento que en algunos casos puede llegar a extenderse en el tiempo, y resultando imprescindible proporcionar algún tipo de retroalimentación (feedback) que le permita conocer su evolución. El objetivo de esta tesis se centra en realizar un estudio sobre las posibles consecuencias de utilizar un feedback basado en técnicas de realidad virtual en el entrenamiento de los sistemas BCI. Para realizar dicho estudio y poder validarlo, ha sido necesario comparar los resultados obtenidos al emplear estas técnicas con aquellos obtenidos a través de un sistema BCI de referencia basado en un feedback convencional como la extensión de una barra horizontal. Dicho estudio comparativo ha obligado por lo tanto a desarrollar dos tipos diferentes de interfaces cerebro-computadora y en definitiva, realizar el trabajo en dos fases diferentes. En una primera fase, se ha desarrollado y evaluado el sistema BCI de referencia permitiendo obtener resultados que pudieran ser comparados con la interfaz objeto de este trabajo. En una segunda fase, se ha desarrollado y evaluado el sistema BCI basado en técnicas de realidad virtual. Los resultados obtenidos muestran un comportamiento distinto en la respuesta de los sujetos logrando un mejor control de las señales EEG, en especial durante el periodo del feedback. Parece que el uso de una interfaz que resulte más familiar y más atractiva a los sujetos permite lograr una mayor motivación e integración de éstos y puede mejorar los resultados de clasificación, al mismo tiempo que permite una mayor y más rápida adaptación del sujeto al paradigma de entrenamiento

EEG-based communication via dynamic neural network models

The overall aim of this research is to develop an EEGbased computer interface. In this paper we report on an offline analysis of EEG data recorded from 7 subjects performing two different pairs of cognitive tasks; motor imagery versus a baseline task and motor imagery versus a maths task. For the imagery versus baseline pairing, discrimination was good in three subjects, marginal in two and not possible in the other two. For the imagery versus maths pairing, discrimination was very good in two subjects, good in 4 and marginal in one. The data was analysed using lagged-AR feature vectors and a Bayesian logistic regression classifier with temporal smoothing. Enhanced spectra are shown highlighting differential spectral activity for each task pairing. The results suggest that combinations of different task pairings and dynamic neural network models have the potential to drastically reduce the time it takes for a new user to learn to use an EEG-based computer interface. I. Introduction Th..