Brain-controlled unmanned aerial vehicle method based on steady-state visual evoked potential brain-computer interface

Abstract

The invention discloses a brain-controlled unmanned aerial vehicle method based on a steady-state visual evoked potential brain-computer interface. The method comprises the steps that an SSVEP stimulation interface is constructed, a Keep instruction is set to be arranged in the center of the stimulation interface and used for keeping the flight state of an unmanned aerial vehicle and achieving a pseudo-asynchronous control effect, and when the stimulation interface works in an online mode, an SSVEP stimulation fragment with the duration [delta]t continuously flickers till the process that a user controls the unmanned aerial vehicle is finished; an EEG data module processes the acquired electroencephalogram signals of the duration[delta]t based on task related component analysis and a sliding time window method of a linear discrimination model, and maps classification results to different unmanned aerial vehicle flight control instruction vectors; and an unmanned aerial vehicle flight control module fuses the control instruction vectors output by the m+1 EEG data modules and transmits the fused instruction vectors to the unmanned aerial vehicle to realize flight control of the unmanned aerial vehicle. Flexible, stable and robust control over the unmanned aerial vehicle is achieved through the brain-computer interface, and a user does not depend on two hands any more when controlling the unmanned aerial vehicle.

Description

technical field [0001] The invention relates to the field of brain-controlled unmanned aerial vehicles, in particular to a brain-controlled unmanned aerial vehicle method based on a steady-state visual evoked potential brain-computer interface. Background technique [0002] Brain-computer interface (BCI) refers to the establishment of a direct communication path between the human brain and external devices without relying on peripheral nerves and muscles. It provides an efficient entry into communication with the outside world for those with physical disabilities but full consciousness. In recent years, non-invasive scalp electroencephalography (EEG) has been widely used in brain-computer interface systems due to its relatively low hardware cost and high temporal resolution. The most commonly used brain control signals are P300, sensorimotor rhythm (sensorimotor rhythm, SMR) and steady-state visual evoked potential (steady-state visual evoked potential, SSVEP). SSVEP is a ...

AI Technical Summary

Problems solved by technology

[0005] 1. There are few flight control commands for drones, and the control is not flexible enough; 2. The information transmission rate of the brain-computer interface is low, and precise real-time control cannot be achieved; 3. Most researches control drones from the third perspective, lacking immersive experience

Images