Brain Controlled Car using Deep Neural Network

  • Amit Kumar
  • Aishwarya Bhisikar
  • Ajay Kumar Pandit
  • Ketan Singh
  • Ajitkumar Shitole


Brain Computer Interface (BCI) is
the modern technology which uses the brain
neural activity to control the machines,
robots, etc. This paper focuses on processing
of the signals received from the
Electroencephalography (EEG) headset into
directions using Artificial Neural Network.
The main aim is to control a car using
Neurosky Mindwave Mobile headset. The
goal is to help people suffering from
disabilities and motion syndrome. The EEG
headset is placed on the head of the user and
signals like Alpha1, Alpha2, attention level,
meditation level, blink and raw signals are
recorded. The pre-processed signal and feedforward
Artificial Neural Network are used
for classification. ANN is developed in 3
layers: input, hidden and output. The six
signals received from Neurosky Mindwave
headset is given as input to ANN. This will
identify the direction of the car to move
forward, backward, left, right or stop.

Keywords: Brain Computer Interface, Electroencephalography, Neurosky Mindwave, Artificial Neural Network.


[1] T.A.Izzuddin , M.A.Ariffin , Z.H.Bohari ,
R.Ghazali , M.H.Jali ,”Movement Intention
Detection Using Neural Network for
Quadriplegic Assistive Machine” ,2015 IEEE
International Conference on Control System,
Computing and Engineering, 27 - 29 November
[2] K. Amarasinghe, D.Wijayasekara, M.Manic , “ EEG
Based Brain Activity Monitoring using Artificial Neural
Networks” ,2014 7th International Conference on Human
System Interactions (HSI)
[3] J. Katona, I. Farkas, T. Ujbanyi, P. Dukan, A.
Kovari, “Evaluation Of The Neurosky MindFlex
[4] EEGHeadset Brain Waves Data” , SAMI 2014
IEEE 12th International Symposium on Applied
Machine Intelligence and Informatics
[5] Vinay Kumar Karigar Shivappa, Brian Luu, Marco
Solis, and Kiran George ,”Home Automation System
using Brain Computer Interface Paradigm based on
Auditory Selection Attention” , 2018 IEEE International
Instrumentation and Measurement Technology
Conference (I2MTC)
[6] Athanasios Vourvopoulos, Fotis Liarokapis , “Brain-
Controlled NXT Robot: Tele-operating aRobot
through Brain Electrical Activity”, Conference: Games
and Virtual Worlds for Serious Applications (VSGAMES)
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How to Cite
Kumar, A., Bhisikar, A., Pandit, A. K., Singh, K., & Shitole, A. (2019). Brain Controlled Car using Deep Neural Network. Asian Journal For Convergence In Technology (AJCT). Retrieved from