Virtual reality hardware and graphic display options for brain–machine interfaces

Abstract

Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where ablebodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing.

Viewing medium affects arm motor performance in 3D virtual environments

“…Virtual reality applications are well-suited to shaping motor output by providing optimal learning conditions that combine extrinsic sensory feedback from the environment with intrinsic sensory feedback from the moving limb [ 7 - 9 ]. Since the quality of the viewing environment may alter how movement is produced [ 10 , 11 ], it is essential to know whether movements performed in a VE are similar to those performed in an equivalent physical environment (PE). Kinematics of pointing, reaching and grasping movements made in 2D and 3D VEs have been compared to those made in PEs in a series of studies by Levin and colleagues [ 12 - 15 ] in healthy subjects and in those with chronic post-stroke hemiparesis.…”

Technology for mobility in SCI 10 years from now

“…34 In 10 years VR will likely be less expensive, more immersive and better able to respond to any biological signal such as brain computer interface. 35 VR in one form or another will likely be a normal part of rehabilitation, increasing the fun of repetitive movement and motivating patients. Research in 2020 is likely to involve the best way to use this potentially potent tool from a neuroplasticity, motivation and motor recovery perspective.…”

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

“…Yet in other cases, for example stimulation of the cortex directly, removing stimulus artifacts from MEG signals would be particularly challenging. Advanced ways to display visual feedback are being increasingly used for BMI applications (Marathe et al 2008). Paradigms using virtual reality models of a human are useful particularly for neurofeedback applications because they can induce a sense of ownership over the virtual body (Bailenson et al 2003;Stanney et al 2003) and it was observed that when participants empathize with the virtual person they perform tasks better (Friedman 2001).…”