Vehicle Static Function Control Technology Based On Brain-computer Interface

Brain-computer interface(BCI)technology is a new type of human-computer interaction,which allows people to communicate information directly through brain consciousness activities and external environmental devices.This method of communication can pass through the nervous system and related tissues of the human body,thereby helping patients with mobility disorders to reestablish contact with the external environment.With the continuous deepening of the research field,the application scope of BCI has also begun to expand into the normal life activities of healthy people.At the same time,in the process of rapid development of autonomous driving technology,there is a large gap from the human brain intelligence in terms of "brain capacity" and the ability to make decisions in complex environments.Combining BCI with autonomous driving technology,integrating brain intelligence into the automated driving system,assisting the system to solve the problem of decision-making and judgment in complex environments,and designing BCI-based static functions of vehicle control technology to improve system automation and intelligence is of great significance.This article focuses on this goal.First,according to the data structure of the CAN bus message transmission process of the car,the corresponding instructions of multiple electronic control units on the car are analyzed,and multi-threading and synchronization lock processing are used to ensure the fast operation and stability of the system.Based on this,a CAN analyzer is selected as a data converter,and the output instructions of the BCI are transferred into electrical signals,and the signals are transmitted to the car through the CAN bus.A stable and effective brain-controlled car control system was initially established.Then,the control paradigm of brain-controlled car system based on steady state visual evoked potential(SSVEP)is designed.Six control objects are selected: low beam,high beam,horn,wiper,warning light,and door lock.By optimizing the control interface and stimulus frequency,the paradigm can be applied to real scenes.Experiments have determined that the average accuracy rate of each target selection task is higher than 80%,and the average information transmission rate exceeds 22 bits / min,which verifies the feasibility of brain-controlled vehicles based on SSVEP.Finally,we continue to improve the above paradigm.In order to make the paradigm meet the needs of realistic control,we optimized the synchronous paradigm control algorithm and proposed an asynchronous control system based on SSVEP.Through constant selection of thresholds,the system can better classify signals and output control commands.Its overall average accuracy rate is above 80%,which shows the reliability and application prospects of this brain-controlled car.