Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG

Ramaswamy Palaniappan, Surej Mouli, Howard Bowman, Ian McLoughlin

Research output: Contribution to journalArticlepeer-review


Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated when braking, are the primary mechanism to alert following drivers to the need to reduce speed or brake. This paper develops a novel brain response approach to measuring subject reaction to different brake light designs. A variety of off-the-shelf brake light assemblies are tested in a physical simulated driving environment to assess the cognitive reaction times of 22 subjects. Eight pairs of LED-based and two pairs of incandescent bulb-based brake light assemblies are used and electroencephalogram (EEG) data recorded. Channel Pz is utilised to extract the P3 component evoked during the decision making process that occurs in the brain when a participant decides to lift their foot from the accelerator and depress the brake. EEG analysis shows that both incandescent bulb-based lights are statistically slower to evoke cognitive responses than all tested LED-based lights. Between the LED designs, differences are evident, but not statistically significant, attributed to the significant amount of movement artifact in the EEG signal.
Original languageEnglish
Number of pages6
JournalIEEE Transactions on Intelligent Transportation Systems
Early online date2 Jul 2021
Publication statusE-pub ahead of print - 2 Jul 2021

Bibliographical note

Copyright: Authors, 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

Funding: Road Safety Trust under Grant RST 90_4_18.


  • Brakes
  • Electroencephalography
  • Vehicles
  • Hardware
  • Brain modeling
  • Light emitting diodes
  • Shape


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