Implementation of Self Driving Car to Assist Physically Challenged People by Analyzing EEG Signals.

Abstract

The modern technology which is meant to provide communication between our brain and the car is BCI (Brain computer interface). Its goal is to help persons who are disabled or have trouble moving due to muscle cramps. It uses an EEG (Electroencephalogram) headset to record data, classifies and demonstrate the set of data on the hardware and attain required commands on the robotic car reached from the given order. The data is moved through a Zigbee module in that period Arduino will executes the commands. This interfacing techniques can be used to unravel brain activity into commands to control outer devices. Electroencephalogram (EEG) signals can be acquired for implementation of movements. To record the fresh signals, the EEG headset is placed on the user's head. For categorization, the preliminary signal and feed forward Artificial Neural Network are used.  The ANN comprises three layers: input, which is used to feed data, hidden, which is the second phase, and output. The six signals acquired from the EEG headset are sent into ANN. This will recognise whether the car is moving forward or backward, and whether it is turning left, right, or stopping.