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ABSTRACT The present study aimed to determine the optimum response time (RT) needed to identify images of everyday objects when filtered using different spatial frequency bands. Subjects were randomly presented with different images of familiar objects that were both serialized and progressive in their spatial frequencies. The time needed to recognize them was then measured. The results showed that the optimum RT for identifying an image filtered in different spatial frequency bands was approximately 2000 ms of exposure. Specifically, stimuli presented using spatial frequency bands with Gaussian filters of variance V26-V32, which were familiar and of medium size to the viewer, were recognized in a mean time of 2126 ms.
RESUMEN El presente estudio tiene como objetivo determinar el tiempo de respuesta óptimo (RT) necesario para identificar imágenes de objetos cotidianos cuando se filtran utilizando diferentes bandas de frecuencias espaciales. A los sujetos se les presentaba aleatoriamente diferentes imágenes de objetos familiares cuyas bandas de frecuencia eran progresivamente serializadas. Se midió el tiempo necesario para reconocerlos. Los resultados mostraron que la RT óptima para identificar una imagen filtrada en diferentes bandas de frecuencias espaciales fue de aproximadamente 2000 ms de exposición. En concreto, los estímulos presentados utilizando bandas de frecuencias espaciales con filtros gaussianos de varianza V26-V32, que eran familiares y de tamaño medio para el espectador, se reconocieron en un tiempo medio de 2126 ms.
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Tiempo de Reacción , Percepción VisualRESUMEN
Introduction In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.
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Objective To analyze the energy of rolling manipulation in different frequency bands and find the features of rolling manipulation dynamics. MethodThe force signals of rolling manipulation of six experts and six beginners were measured and divided into different frequency bands by wavelet transform to calculate the energy. Through statistical analysis, 18 characteristic quantities of horizontal force or vertical force were created and the overall evaluation coefficient R was proposed. ResultsAbout 70% of experts’ rolling manipulation energy was found in 0~0.406 25 Hz and about 20% energy in 1.625~3.25 Hz. The overall evaluation coefficient R of 6 experts was over 0.70, while R of beginners was below 0.70, which showed the difference was significant. ConclusionsThe energy distribution of rolling manipulation reflects the characteristics of softness and periodicity. If the rolling manipulation is in accordance with the manipulative requirement and the overall evaluation coefficients R is over 0.70, it could be said that the operator masters the rolling manipulation well.