Cortical and subcortical mechanisms of brain‐machine interfaces

Abstract

Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.

Neural mechanisms of training an auditory event‐related potential task in a brain–computer interface context

“…The SMA has been shown to play a role in SCP and motor imagery BCIs in humans and in studies with monkeys controlling a neuronal interface (Carmena et al, ; Halder et al, ; Hinterberger et al, ; Marchesotti et al, ). Interestingly, neural activation in the SMA has been found to be correlated with performance in auditory attention tasks, similar to the previous findings relating to motor imagery BCI performance (Seydell‐Greenwald, Greenberg, & Rauschecker, ).…”

“…The SMA has also been shown to be active during real‐time feedback (Zich et al, ). Marchesotti et al () confirmed the role of SMA during motor imagery tasks, but also pointed out the contribution of areas outside the sensorimotor cortex to the BCI task, in particular the posterior parietal cortex and insular cortices.…”

Success and failure of controlling the real‐time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum

“…Marchesotti et al () detected a selective activation increase in the striatum during motor imagery with neurofeedback when comparing meta‐analytic activation maps of motor imagery with and without providing neurofeedback and Johnston, Boehm, Healy, Goebel, and Linden () had reported increased activation in the ventral striatum with progression in neurofeedback training for up‐regulating negative affect by providing neurofeedback from individual areas that showed increased activation in response to negative affective image. In congruence with these reported activation increases in the striatum during neurofeedback, several theoretical frameworks note that BCI control/neurofeedback rewards subjects for a certain mental operation or neural state, notably by underling the crucial involvement of operant/instrumental conditioning in neurofeedback (Fetz, ), by interpreting BCI control training as skill learning that is heavily dependent on plasticity in the basal ganglia (Birbaumer, Ruiz, & Sitaram, ) or by underlining the importance of feedback loops for biofeedback learning in general (Lacroix & Gowen, ).…”

Agency and responsibility over virtual movements controlled through different paradigms of brain–computer interface

“…The role of motor areas in the sense of agency is theoretically supported by a computational model of agency, the comparator model (Frith, Blakemore, & Wolpert, 2000), which is based on a model for sensorimotor integration (Wolpert, Ghahramani, & Jordan, 1995) and describes agency as the result of a matching between predicted and actual sensory feedback of a planned motor action (Blakemore, Wolpert, & Frith, 2000;Frith et al, 2000). Further, brain regions related to BCI control not related to the comparator model have been identified in the basal ganglia, the anterior cingulate cortex and the left superior frontal gyrus (Marchesotti et al, 2017).…”