Abstract: In accordance with one embodiment of the present disclosure, a method includes measuring brain activity for a target frequency and a second harmonic frequency based on a default value of display parameters for a plurality of icons, determining whether a strength of the target frequency and the second harmonic frequency are below a threshold level, and modifying one or more display parameters in response to the strength of the target frequency and the second harmonic frequency being below the threshold level.

Abstract: System, methods, and other embodiments described herein relate to controlling display attributes within a visual interface for enhancing stimuli and responses of a user. In one embodiment, a method includes selecting display attributes related to a first layer of an image and a second layer of the image, wherein the image is associated with an interface to control a component. The method also includes oscillating, at a frequency, between first values of the display attributes for displaying the first layer. The method also includes oscillating, at a harmonic of the frequency, between second values of the display attributes for displaying the second layer in parallel with the first layer until detecting a selection associated with the interface.

Abstract: In accordance with one embodiment of the present disclosure, a method includes generating a plurality of icons, wherein each icon has a target frequency unique from each other, receiving brain activity data based on an epoch, generating a reference signal based on the epoch, calculating correlation coefficients between the brain activity data and the reference signal, wherein the correlation coefficients are calculated in a window that is within ±0.5 Hz of the target frequencies, including endpoints, determining a confidence score based on the correlation coefficients and the epoch, and determining a selected icon among the plurality of icons based on the correlation coefficients in response to the confidence score surpassing a threshold confidence score.

Abstract: In accordance with one embodiment of the present disclosure, a method includes measuring brain activity for a target frequency and a second harmonic frequency based on a default value of display parameters for a plurality of icons, determining whether a strength of the target frequency and the second harmonic frequency are below a threshold level, and modifying one or more display parameters in response to the strength of the target frequency and the second harmonic frequency being below the threshold level.

Abstract: In accordance with one embodiment of the present disclosure, a method includes generating a plurality of icons, wherein each icon has a target frequency unique from each other, receiving brain activity data based on an epoch, generating a reference signal based on the epoch, calculating correlation coefficients between the brain activity data and the reference signal, wherein the correlation coefficients are calculated in a window that is within ±0.5 Hz of the target frequencies, including endpoints, determining a confidence score based on the correlation coefficients and the epoch, and determining a selected icon among the plurality of icons based on the correlation coefficients in response to the confidence score surpassing a threshold confidence score.

Abstract: System, methods, and other embodiments described herein relate to controlling display attributes within a visual interface for enhancing stimuli and responses of a user. In one embodiment, a method includes selecting display attributes related to a first layer of an image and a second layer of the image, wherein the image is associated with an interface to control a component. The method also includes oscillating, at a frequency, between first values of the display attributes for displaying the first layer. The method also includes oscillating, at a harmonic of the frequency, between second values of the display attributes for displaying the second layer in parallel with the first layer until detecting a selection associated with the interface.

Abstract: A method, system, and non-transitory computer readable medium describing an autoencoder that creates a reduced feature space from healthy power electronics devices for training. Devices under test are then encoded and compared to the encoded features of the healthy devices to determine health of the other devices. Contextual information is used to build multiple models that compare power electronics devices from similarly operated vehicles with one another.

Abstract: A method, system, and non-transitory computer readable medium describing an autoencoder that creates a reduced feature space from healthy power electronics devices for training. Devices under test are then encoded and compared to the encoded features of the healthy devices to determine health of the other devices. Contextual information is used to build multiple models that compare power electronics devices from similarly operated vehicles with one another.

Abstract: Apparatuses, systems, and methods execute an iterative training process that executes an iterative training process to train an Ensemble Learning Model based on a plurality of observations associated with electronic devices so that the Ensemble Learning Model predicts conditions of the electronic devices. The electronic devices are associated with a vehicle. The iterative training process includes iteratively training the Ensemble Learning Model based on different groups of the plurality of observations during different iterations, wherein the different groups of the plurality of observations are associated with different subsets of the electronic devices, and generating an Out-of-Bag score based on whether the Ensemble Learning Model correctly predicts conditions of the electronic devices based on observations of the plurality of observations that were previously unutilized to train the Ensemble Learning Model.