Brain-Machine Interface for drivers
Nissan is undertaking this pioneering work in collaboration with the École Polytechnique Fédérale de Lausanne in Switzerland (EPFL). Far reaching research on Brain Machine Interface (BMI) systems by scientists at EPFL already allows disabled users to manoeuvre their wheelchairs by thought transference alone. The next stage is to adapt the BMI processes to the car – and driver – of the future.
Professor José del R. Millán, leading the project, said: “The idea is to blend driver and vehicle intelligence together in such a way that eliminates conflicts between them, leading to a safer motoring environment.”
Although thought control – via brain-machine interface – is well established in the scientific world, the levels of concentration needed are exceptionally high. The Nissan/EPFL collaboration is developing systems that go to the next stage using
statistical analysis to predict a driver’s intentions and to evaluate a driver’s cognitive state relevant to the driving environment.
Using brain activity measurement, eye movement patterns and by scanning the environment around the car in conjunction with the car’s own sensors, it should be possible to predict what the driver plans to do – be it a turn, an overtake, a lane
change – and then assist with the manoeuvre in complete safety, thus improving the driving experience.
Media coverage:
CarBuzz Future Nissan Cars Will Be Able To Read Your Mind (Jan 4, 2018)
Nissan Global Rooms Press Release Nissan Brain-to-Vehicle technology redefines future of driving. Latest breakthrough in Nissan Intelligent Mobility promises cars that learn from driver (January 3, 2018)
Fox News Nissan Developing Mind-Reading Cars (October 10, 2016)
MITTech Review Nissan’s Cars Will Read Your Mind 09/(2011)
Autoblog Greece Nissan working on a car that reads your mind (9/2011)
FastCompany Nissan’s Future Mind-Reading Cars Won’t Steer You Wrong (9/2011)
La Repubblica L’auto si guida col pensiero Nissan accetta la sfida (9/2011)
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JOURNAL ARTICLES
Aydarkhanov R., Ušćumlić M., Chavarriaga R., Gheorghe L.A., Millán J.d.R. (2020). Spatial covariance improves BCI performance for late ERPs components with high temporal variability. Neural Engineering, 17:036030.
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Detailed record – Full text – View at publisher
H. Zhang; R. Chavarriaga; Z. Khaliliardali; L. A. Gheorghe; I. Iturrate et al. : EEG-based decoding of error-related brain activity in a real-world driving task; Journal of Neural Engineering. 2015. DOI : 10.1088/1741-2560/12/6/066028.