Brain-controlled drone method based on steady-state visual evoked potential brain-computer interface

Abstract

The invention discloses a brain-controlled UAV method based on a steady-state visual evoked potential brain-computer interface, including: constructing a SSVEP stimulation interface and setting a "Keep" command in the center of the stimulation interface to maintain the flight state of the UAV , to play a "pseudo-asynchronous" control effect. When the stimulation interface works in the online mode, the SSVEP stimulation segment with a duration of Δt will flash continuously until the end of the user's control of the drone; the EEG data module is based on task-related component analysis and linear The sliding time window method of the discriminant model processes the collected EEG signals with a duration of Δt, and maps the classification results to different UAV flight control instruction vectors; the UAV flight control module outputs m+1 EEG data modules The control instruction vector is fused, and the fused instruction vector is transmitted to the UAV to realize the flight control of the UAV. The invention utilizes the brain-computer interface to realize flexible, stable and robust control of the drone, and the user no longer relies on both hands when controlling the drone.

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

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