EEG signal classification to detect left and right command using artificial neural network (ANN)

In this study, the right and left commands explored are based on the actual movement oflifting either left or right hand and the imaginary movement of lifting either left or right handFor this initial study, EEG signals recorded based on the actual physical movements will becollected as the raw data, as well as the EEG signals recorded when imaginary movements areperformed. In the scope of this research, the EEG processing focuses on analyzing two different features namely SD and ESD. These features are used as inputs to be classified by the ANN classifier. The performance of this classifier is then evaluated by measuring its accuracy in distinguishing the different interpreted commands. Based on findings from the conducted analysis, we found that PSD is the best feature to be fed as input to the ANN classifier with a high accuracy of 93% compared to when ESD feature is used as the input.Keywords: BCI; EEG; classification; Energy Spectral Density (ESD); Power SpectralDensity (PSD); Artificial Neural Network (ANN)

Performance of modified power spectral density features in EEG signal classification

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Student opinion survey on delivery of ECE431: Computer programming undergraduate course

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Gait recognition using kinect and locally linear embedding

This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person’s identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification accuracy result was used for comparison between both. Several MLP and LLE properties were tested to find the optimal number of setting that can improve the MLP performance. Based on the two methods used, the neural network implemented with LLE showed the better accuracy compared to the neural network alone.Keywords: locally linear embedding; neural network; multilayer perceptron

Performance evaluation of faster R-CNN on GPU for object detection

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Recognition of human gait in oblique and frontal views using Kinect

This study describes the recognition of human gait in the oblique and frontal views using novel gait features derived from the skeleton joints provided by Kinect. In D-joint, the skeleton joints were extracted directly from the Kinect, which generates the gait feature. On the other hand, H-joint distance is a feature of distance between the hip joint with other skeleton joints. Prior to the gait feature extraction, the skeleton joints provided by Kinect were pre-processed in order to standardize the size of the skeleton image as well as to detect the gait feature within a full gait cycle. To classify gait patterns according to its own group, a multi-layer perceptron was employed in the pattern recognition stage. Results show that a perfect recognition of human gait (100%) was attained for the frontal view using the feature of H-joint distance at the optimal multi-layer perceptron (20 hidden units)Keywords: human gait recognition; Kinect; oblique view; frontal view; gait cycl

Infant asphyxia detection using autoencoders trained on locally linear embedded-reduced Mel Frequency Cepstrum Coefficient (MFCC) features

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