Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface

Researchers from University of Minnesota have taken the brain-computer interface a bit further by giving the user full control of the movements of a helicopter in a virtual environment. g087hpiuj Brain Computer Interface Steers Helicopter Movements in 3DAlthough experiments with brain-computer interfaces have been going on for quite some years already, most tasks have involved one dimensional or two dimensional movement (we recently covered mind controlled slot car racing). Three-dimensional control has been achieved with the use of invasive brain electrodes, but non-invasively this has been notoriously hard to achieve.

Here the researchers have achieved just that by using the EEG signal from a 64-channel EEG cap to record and decode sensorimotor rhythms induced from motor imaginations. The subjects first went through a training utilizing left/right arm, leg, tongue, and rest imaginations to move a cursor first in 1D and the n in 2D, before moving on to the 3D helicopter task. They even managed to differentiate between gross and fine movements on a continuous scale, allowing for fast and precise control of helicopter movement.

As a final task, the subjects controlled the helicopter with the goal of flying through randomly positioned rings. Impressively, they successfully acquired over 85% of presented targets. Although the authors foresee applications in neuroprosthetics and rehabilitative medicine, we are envisioning controlling real-life model helicopters or drones instead. The results were published online in PLoS ONE.

Brain-computer interfaces (BCIs) allow a user to interact with a computer system using thought. However, only recently have devices capable of providing sophisticated multi-dimensional control been achieved non-invasively. A major goal for non-invasive BCI systems has been to provide continuous, intuitive, and accurate control, while retaining a high level of user autonomy. By employing electroencephalography (EEG) to record and decode sensorimotor rhythms (SMRs) induced from motor imaginations, a consistent, user-specific control signal may be characterized. Utilizing a novel method of interactive and continuous control, we trained three normal subjects to modulate their SMRs to achieve three-dimensional movement of a virtual helicopter that is fast, accurate, and continuous. In this system, the virtual helicopter’s forward-backward translation and elevation controls were actuated through the modulation of sensorimotor rhythms that were converted to forces applied to the virtual helicopter at every simulation time step, and the helicopter’s angle of left or right rotation was linearly mapped, with higher resolution, from sensorimotor rhythms associated with other motor imaginations. These different resolutions of control allow for interplay between general intent actuation and fine control as is seen in the gross and fine movements of the arm and hand. Subjects controlled the helicopter with the goal of flying through rings (targets) randomly positioned and oriented in a three-dimensional space. The subjects flew through rings continuously, acquiring as many as 11 consecutive rings within a five-minute period. In total, the study group successfully acquired over 85% of presented targets. These results affirm the effective, three-dimensional control of our motor imagery based BCI system, and suggest its potential applications in biological navigation, neuroprosthetics, and other applications.

Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface

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