Data from: Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

Brain-computer interfaces (BCIs) using signals acquired with intracortical
implants have achieved successful high-dimensional robotic device control
useful for completing daily tasks. However, the substantial amount of
medical and surgical expertise required to correctly implant and operate
these systems greatly limits their use beyond a few clinical cases. A
noninvasive counterpart requiring less intervention that can provide
high-quality control would profoundly improve the integration of BCIs into
the clinical and home setting. Here, we present and validate a noninvasive
framework using electroencephalography (EEG) to achieve the neural control
of a robotic device for continuous random target tracking. This framework
addresses and improves upon both the “brain” and “computer” components by
increasing, respectively, user engagement through a continuous pursuit
task and associated training paradigm and the spatial resolution of
noninvasive neural data through EEG source imaging. In all, our unique
framework enhanced BCI learning by nearly 60% for traditional center-out
tasks and by more than 500% in the more realistic continuous pursuit task.
We further demonstrated an additional enhancement in BCI control of almost
10% by using online noninvasive neuroimaging. Last, this framework was
deployed in a physical task, demonstrating a near-seamless transition from
the control of an unconstrained virtual cursor to the real-time control of
a robotic arm. Such combined advances in the quality of neural decoding
and the practical utility of noninvasive robotic arm control will have
major implications for the eventual development and implementation of
neurorobotics by means of noninvasive BCI.