Toyota Motor Engineering & Manufacturing North America, Inc.

University of Connecticut

Hossein Hamidi Shishavan

Insoo Kim

Ercan Mehmet Dede

Muhamed K Farooq

Kia Golzari

Danny J Lohan

Mohammad Behzadi

A vehicle system includes a controller programmed to display a plurality of icons on a heads-up-display (HUD) of the vehicle, receive electroencephalography (EEG) data from a driver of the vehicle, perform a Fast Fourier Transform of the EEG data to obtain an EEG spectrum, input the EEG spectrum into a trained machine learning model, determine which of the plurality of icons the driver is viewing based on an output of the trained machine learning model, and perform one or more vehicle operations based on the output of the trained machine learning model.

Closed-loop real time SSVEP-based heads-up display to control in vehicle features using deep learning

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.

Methods, systems, and non-transitory computer-readable mediums for SSVEP detection optimization

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.

Methods, systems, and non-transitory computer-readable mediums for SSVEP detection

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New Britain, CT, United States of America

Hossein Hamidi Shishavan