What I failed to appreciate, however, was that “within my lifetime,” which I had optimistically hoped would extend to somewhere past 2040 or so, is starting to look more like “within the next couple of decades.”
Eric C. Leuthardt, M.D., an assistant professor of neurological surgery at the WUSTL school of Medicine, and Daniel Moran, Ph.D., assistant professor of biomedical engineering,were able to decode signals from a sensory grid implanted on the surface of a teenager’s brain, and train the teen to control (what else?) a video game using only his imagination. (See a video of the truly wired teen here.)
With the increasing use of Functional MRI as a tool to understand cognitive processes, Brain-computer interface technologies are advancing at a staggering rate alongside dramatic improvements in neurosurgery. This latest effort was able to leverage some of the latest neuro-surgery techniques used to treat epilepsy, wherein a thin grid of electrodes is laminated to the actual surface of the brain in order to triangulate the source location from which seizure-inducing brain activity originates.
From the original paper Figure 1. Examples of electrode placement and ECoG signals. (a) Intra-operative placement of a 64-electrode subdural array. (b) Post-operative lateral skull radiograph showing grid placement. (c) Raw ECoG signals during control of cursor movement. Black and red traces are from one of the electrodes that controlled cursor movement and are examples for the patient resting and imagining saying the word ‘move’, respectively. (d) Spectra for the corresponding conditions for the final run of online performance.
Figure 4 from the original paper shows: ECoG correlations with joystick movement direction before and during movement. (a) Left and center panels: time courses for left and right movements, respectively. Right panel: the absolute value of the difference between left and right time courses. Movement direction is reflected in ECoG across a wide frequency range, including frequencies far above the EEG frequency range. (b) The correlation between the signal shown in (a) and movement direction over the period of movement execution. (c) Correlation for a single electrode location versus the remote reference electrode. The μ rhythm activity predicts movement direction. In (b) and (c), and indicate negative correlation and positive correlation, respectively, with the amplitude of left movement minus right movement. (d) Average final cursor positions predicted by a neural network from ECoG activity are close to the actual average final cursor positions.
Figure 2 from the original paper: ECoG control of vertical cursor movement using imagination of specific motor or speech actions to move the cursor up and rest to move it down. The electrodes used for online control are circled and the spectral correlations of their ECoG activity with target location (i.e., top or bottom of screen) are shown. Grids for patients B, C and D are green, blue and red, respectively. The substantial levels of control achieved with different types of imagery are evident. The three-dimensional brain model was derived from MRI data.
It is really interesting to start thinking about computing problems like wireless interfaces (Bluetooth?), power supplies (capacitive coupling of microwaves far from H2O resonant frequencies?), and cooling (blood?) when it has to be IN YOUR HEAD!
Who’s up for really getting wired?
Don’t miss the original paper entitled “A brain–computer interface using electrocorticographic signals in humans” and the WUSTL PR page with the live video.