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 Flow, a full-head coverage, time-domain (TD) fNIRS system to detect hemodynamic changes in the brain.

Kernel Flow takes advantage of the relative transparency of the skull and brain tissue to near-infrared light by beaming photons through the skull and measuring their scattering and absorption, allowing inference about blood flow and oxygenation (hemodynamics). It offers the resolution and sensitivity of state-of-the-art hemodynamic systems across the top layers of cortical tissue but can be manufactured for only a few thousand dollars and is commercially scalable.

What is “time domain” fNIRS? Traditional "continuous wave" (CW) fNIRS devices apply light to the head continuously, which then scatters throughout and is detected at various locations upon exiting the head. Changes in the detected light intensity allow inference of optical-property changes inside the head, like those resulting from neural activity. Time-domain systems like Kernel Flow capture a much richer signal by applying the light in short pulses and capturing precisely the arrival time distribution of scattered photons for each pulse. On average, photons that arrive later travel deeper through the tissue, which reveals additional depth-dependent information about the optical properties of the tissue - allowing for more detailed inference of brain activity.

Kernel has architected a new class of TD-fNIRS system that maintains the performance of research-grade systems while reducing the footprint to a small module. All of the components in the Flow optical modules are designed to leverage the well-established supply chains of the consumer electronics industry. Through a combination of low-cost and high-volume manufacturing, we will be able to achieve a scale of Flow systems that exceeds all existing research-grade fMRI, EEG, and fNIRS systems.

For more technical details about Kernel Flow, including more information on why TD 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.