Classification of Imaginary Motor Task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms / Clasificación de una Motricidad Imaginaria por medio de Señales Electroencefalográficas: Una Comparación de Métodos de Selección de Características y Algoritmos de Clasificación

Abstract: In this work, a Brain Computer interface able to decode imagery motor task from EEG is presented. The method uses time-frequency representation of the brain signal recorded in different regions of the brain to extract important features. Principal Component Analysis and Sequential Forward Selection methods are compared in their ability to represent the feature set in a compact form, removing at the same time unnecessary information. Finally, two method based on machine learning are implemented for the task of classification. Results show that it is possible to decode the mental activity of the subjects with accuracy above 80%. Furthermore, visualization of the main components extracted from the brain signal allow for physiological insights on the activity that take place in the sensorimotor cortex during execution of imaginary movement of different parts of the body.

Resumen: En este trabajo es presentada una Interfaz Cerebro Computadora que tiene la capacidad de decodificar actividades motrices. El método utiliza representación en el dominio de la frecuencia y el tiempo de las señales del cerebro grabadas en distintas regiones de este mismo, con el fin de extraer características importantes. Los métodos: Análisis de Componentes Principales y Selección Secuencial, son comparados en términos de su capacidad para representar características de la señal de una forma compacta, removiendo de esta forma, información innecesaria. Finalmente, dos métodos basados en aprendizaje de máquinas fueron implementados para la clasificación de actividades motrices utilizando solo las señales cerebrales. Los resultados muestran que es posible decodificar la actividad mental en los sujetos con una precisión superior al 80%. Además, la visualización de las componentes principales extraídas de las señales del cerebro permite un analísis de la actividad que toma lugar en la corteza cerebral sensorimotora durante la ejecución de la imaginación de movimientos de distintas partes del cuerpo.

Intubación fibróptica y manejo anestésico de un paciente pediátrico con Síndrome de Freeman-Sheldon / Fiberoptic intubation and anesthetic management of a pediatric patient with Freeman-Sheldon syndrome

Presentamos el caso de un niño con síndrome de Freeman-Sheldon programado para la correccióc quirúrgica de su microstomia. El niño presentabn la característica de "cara de silvador"y deformidades de artrogríposis en manos y pies. Se expone el manejo anestésico y de la vía aéred.

We describe e case of a child with typical clinicalfeaturea of Freeman-Sheldon syndrome (FSS) presentedfor elective surgical correction of microstomia. The anaesthetic cnd airtuay problems encountered erediscussed.

Computer-aided Diagnosis of Gastric Carcinoma B ased on Feature Selection and Probability Neural Network / 现代生物医学进展

Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.

Computer-aided Diagnosis of Gastric Carcinoma B ased on Feature Selection and Probability Neural Network / 现代生物医学进展

Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.

Computer-aided Diagnosis of Gastric Carcinoma B ased on Feature Selection and Probability Neural Network / 现代生物医学进展

Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.