Cost-efficient brainwave controller for automated vehicles route decisions

The cognitive functions of the brain play a very important role in the daily lives of human beings, therefore mental thought patterns, which are represented in neural signals, can be used in enhancing the lives of both healthy and disabled users. Brain Computer Interfaces (BCI) are communication systems, which allow humans to mentally interact with external devices and machines. They detect, record and analyze electroencephalography (EEG) waves, which reflect certain brain functions or commands. In this paper, EEG data readings are collected and classified, using a cost-efficient BCI headset and Neural Network (NN) classifier. The objective is to classify and identify the users route decisions, while being on-board of an automated vehicle. The system takes advantage of the capacity of the automated vehicle for autonomous maneuvering, and the brainwave sensor allows the user to take route decision. Several training experiments were carried-out, moreover, numerous testing experiments were performed. Obtained results show successful classification of the subjects decisions, enhancing the mobility capacity of people with physical disabilities.