Flux

Most methods for recording brain signals without the need for surgery either measure the electromagnetic fields generated by groups of neurons or detect small changes in blood oxygenation, which correlate well to nearby neural activity. After years of rigorous analysis, Kernel has developed systems to detect each: Kernel Flux, which uses magnetometers to measure tiny changes in magnetic fields; and Kernel Flow, which pulses light through the skull and into the bloodstream in order to measure how much oxygen the blood is carrying at any given time.

Introducing Kernel Flux, powered by Optically Pumped Magnetometers for use in Magnetoencephalography (OP-MEG).

Kernel Flux uses alkali vapor sensors to directly detect the magnetic fields generated by collective neural activity in the brain. These sensors are able to detect the extremely small changes in magnetic fields resulting from a brain's intrinsic electrical activity, across the whole head, and in natural environments.

Why OP-MEG? In contrast to electrical signals, the magnetic signature of neural activity directly exits through the skull and scalp without distortion. However, because MEG sensors respond to magnetic fields regardless of their origin, it is critical to distinguish those arising inside the brain from those caused by motion through background magnetic field gradients or other ambient sources such as power lines or nearby electronics like phones or computers. 

A full-head-coverage Kernel Flux system provides 720 channels of magnetometer data and operates without a multilayer, magnetically-shielded room. The large number of channels and active magnetic shielding allows for high-performance noise rejection even in environments with natural user head motion or noisy peripherals. 

Kernel Flux combines the leading edge of several technologies: microfabricated alkali vapor cells, semiconductor lasers, compact optics, high dynamic range real-time control systems, and low-noise electronics. Each Kernel Flux OP-MEG system was designed from the ground up to work as an integrated system optimized around the user's experience for extended periods of time—allowing researchers to capture brain recordings while users watch movies while reclining, during conversation, or while interacting with technology or their nearby environment. 

For more technical details about Kernel Flux, including more information on why our OP-MEG setup and magnetic shielding is so revolutionary, please see here.

Figure 3
(a) Accuracy and (b) ITR as a function of trial time (simulated — actual trials were 2.1 seconds long). Individual sessions are shown and color-coded per subject. In (a) the green shading represents the 99.9% confidence interval for a binomial distribution with success rate 1/36. In both (a) and (b) the gray shading represents a 95% confidence interval for the average across sessions. For reference, the mean and maximum ITRs in a fixed-length trial setting reported in Thielen 2015 are also shown.

Where does the information come from?

We focus the remainder of the results on the best session that we recorded for Speller (highlighted in green in Table 1). How did our algorithm achieve such performance? In particular, is it the case that a single sensor carries most of the information, or is it necessary to look at several sensors simultaneously to arrive at accurate predictions?

First, we looked at the weights that CCA attributed to the different channels and delays (Fig. 4). The weights for the delayed flashes showed an expected sequence of peaks and troughs, reminiscent of a visual evoked field (although, these weights cannot be interpreted as such). The spatial weights on the brain channels show that some channels contribute more than others. However, to assess the true informational content of each sensor, we turn to an analysis in which we try to decode using small subsets of channels.