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

A method, comprising:

• displaying a plurality of icons on a heads-up-display (HUD) of a vehicle;
• receiving electroencephalography (EEG) data from a driver of the vehicle;
• performing a Fast Fourier Transform (FFT) of the EEG data to obtain an EEG spectrum;
• inputting the EEG spectrum into a trained machine learning model;
• determining which of the plurality of icons the driver is viewing based on an output of the trained machine learning model;
• and
• performing one or more vehicle operations based on the output of the trained machine learning model.

A method, comprising:

• receiving training data comprising EEG data collected from a plurality of individual subjects while each subject is viewing specific icons;
• performing an FFT of the training data to obtain EEG spectrum data;
• and
• training a machine learning model to predict which icon the individual subjects are viewing based on the EEG spectrum data.