Automated electrocorticographic electrode localization on individually rendered brain surfaces

Brain-computer interfacing based on cognitive control

OBJECTIVE

Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.

METHODS

Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).

RESULTS

All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.

INTERPRETATION

The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.

Gain of the human dura in vivo and its effects on invasive brain signal feature detection

Invasive brain signal recordings generally rely on bioelectrodes implanted on the cortex underneath the dura. Subdural recordings have strong advantages in terms of bandwidth, spatial resolution and signal quality. However, subdural electrodes also have the drawback of compromising the long-term stability of such implants and heighten the risk of infection. Epidurally implanted electrodes might provide a viable alternative to subdural electrodes, offering a compromise between signal quality and invasiveness. Determining the feasibility of epidural electrode implantation for e.g., clinical research, brain-computer interfacing (BCI) and cognitive experiments, requires the characterization of the electrical properties of the dura, and its effect on signal feature detection. In this paper we report measurements of brain signal attenuation by the human dura in vivo. In addition, we use signal detection theory to study how the presence of the dura between the sources and the recording electrodes affects signal power features in motor BCI experiments. For noise levels typical of clinical brain signal recording equipment, we observed no detrimental effects on signal feature detection due to the dura. Subdural recordings were found to be more robust with respect to increased instrumentation noise level as compared to their epidural counterpart nonetheless. Our findings suggest that epidural electrode implantation is a viable alternative to subdural implants from the feature detection viewpoint.

Brain surface electrode co-registration using MRI and X-ray

Electrocorticographic recording is now being used in a wide variety of experimental settings. We present a simple method which can be used to estimate electrode position with respect to brain gyral anatomy using a pre-implantation MRI and post-implantation coronal and sagittal x-rays. It is semi-automated, with the user manually rotating and scaling an x-ray to fit brain outline, identifying threshold values for brain surface rendering, and clicking on electrodes on an x-ray. Electrode positions can be rapidly identified and rendered in about 20 minutes from start to finish. This approach is useful when the MRI quality is poor, there is no quality CT, but one would like to understand the relationship between experimental result and brain anatomy.