Brain-to-Vehicle (B2V) is a new technology, presented by Nissan at CES 2018, the annual Consumer Electronics Show in Las Vegas, which connects driver’s brain with the vehicle, in order to anticipate his intentions behind the wheel, creating more comfortable and safer driving experiences.
B2V project is based on an innovative brain-computer interface, which has led to great results in brain sensing devices, EEG signal processing, and shared vehicle control. Brain-to-Vehicle technology is able to decode driver’s brain activity thanks to a special wearable device, placed on head: as soon as the driver is about to make a move -braking, steering or performing some other sudden maneuver- B2V detects the impulse emitted by the brain and the driver assistance systems promptly steps in, reducing reaction times and improving manual driving. A great revolution that radically changes the way we interact with vehicles.
Nissan’s Brain-to-Vehicle (B2V) technology
Instead of replacing the driver with an autopilot, the goal of B2V technology is to understand driver’s intentions between 0.2 and 0.8 seconds before being executed. It takes between 0.2 and 0.4 seconds from the time our brain sends a movement order to the moment our muscles execute it. This is the necessary time for information to travel through the nervous system, from brain activation to muscle activation. Between 0.2 and 0.6 seconds before the brain “gives” the order, the brain “prepares” the movement. So it means it’s possible to identify the driver’s intention between 0.4 and 1 second before the driver actually takes an action.
In dangerous situations in which the driver suddenly realizes he must brake, a few seconds pass before he actually does it, but the car thanks to the ability to decode thoughts through B2V technology could immediately intervene starting to brake. At 100 km/h this means saving 27 meters of braking distance, the difference between life and death in the event of a frontal collision.
An important aspect of integrating this information with the vehicle is that it’s based on shared control, which means that the vehicle and the driver share driving, making it more pleasant and safe.
Nissan’s collaboration with Bitbrain
The new Nissan proposal is a milestone achieved in collaboration with Bitbrain, the Swiss Federal Institute of Technology and the Canadian National Research Council. Bitbrain has developed with Nissan an innovative wearable and minimalist EEG neurotechnology with dry sensors, optimized to measure brain activity related to movement.
The EEG system does not require conductive electrolytic gel for its operation, is very comfortable and ergonomic, is designed to capture the driver’s natural behavior and presents a technological appearance and a more discrete design than any other existing EEG technology. It can be placed on average in less than two minutes and can run continuously for up to eight hours, transmitting the driver’s brain activity to the Bluetooth of the vehicle.
The brain-computer interface interprets the driver’s brain activity signals. B2V technology is based on the preparatory brain activity that precedes the execution of the movement to anticipate user intentions. This activity mainly occurs on the motor cortex and it’s identified through two correlated neural EEGs: movement-related cortical potentials (MRCP) and (de) synchronization of the motor event (ERD / ERS).
For other applications with MRCP, read A Glimpse into Vibre: Armonia, Faster rehabilitation with BCI, Artificial Intelligence and Virtual Reality
Real-time EEG decoding of brain activity related to movement anticipation is a very complex process and presents several problems:
- First, the decoding works on the activity generated by a single movement, rather than on an average, so the signal-to-noise ratio is lower (this means that the EEG signal has more noise and it’s more difficult to decode).
- Second, there is no prior knowledge of when the driver is about to brake, and therefore, the driver’s intentions should be continuously decoded (since there is no specific time interval to identify the decoding, the accuracy of the detection is reduced).
- Third, each person’s brain is different and therefore the brain processes related to the movement mentioned above also have different EEG signatures (there are large inter- and intra-personal variations).
These problems are analyzed by algorithms that anticipate movement based on signal processing techniques with automatic learning (machine learning and artificial intelligence) that require a specific calibration phase for each subject.
Lucian Gheorghe, Senior Innovation Researcher of Nissan Intelligent Mobility, states: “The possible applications of this technology are incredible. This research will be a catalyst for Nissan’s innovation in our vehicles over the next few years.”