BrainQ

BrainQ utilizes electrophysiology measurements (EEG, EMG, MEG) to characterize neural oscillatory activity. A growing body of evidence indicates that neural oscillations at specific frequencies are linked to opening neuroplasticity periods10,11, suggesting that using non-invasive brain stimulation (NIBS) techniques to neuromodulate at specific frequencies can influence these oscillations and aid in neurorecovery12–14. These fields have long been studied for their role in disease and recovery, and are similar in both magnitude and frequency to magnetic fields generated about a neuron by the current flows associated with a firing axon16. While humans cannot feel EMF on a sensory level, these fields may have a role in mediating healthy neural dynamics and coordination, which are dependent on synchronous cell firing, and may be mimicked by exogenous exposure to such similar fields. In the case of stroke, as well as other neurological disorders, the oscillatory patterns of unhealthy or impaired individuals are measurably different from those of healthy individuals. With evidence that exposure to specific EMFs can influence neural oscillations15, BrainQ operates on the premise that exposing such unhealthy individuals to specific EMF frequencies associated with healthy functioning may improve network plasticity and functional ability. Thus, BrainQ is developing a treatment to target specific networks in the CNS, utilizing an extremely-low-frequency and low intensity electromagnetic field (ELF-EMF) treatment tuned to specific frequencies, with the goal of repairing damaged neural networks. The diffuse nature of these fields allows for the exposure of the entire CNS and its neural networks. This is an advantage over other forms of NIBS, which typically focus on specific brain regions or segments of the nervous system, and neglect the larger network. BrainQ aims at providing a comprehensive, frequency-tuned treatment to entire networks. The novelty of BrainQ’s investigational treatment lies in the data-driven method we have deployed in order to inform the ELF-EMF frequency parameters. In choosing these parameters, our aim is to select frequencies that characterize motor related neural networks in the CNS, and are related to the disability a person experiences following a stroke or other neurological trauma. To achieve this, we have analyzed a large-scale amount of healthy and non-healthy individuals’ brainwaves (electrophysiology data). Our technology uses explanatory machine learning algorithms to observe the natural spectral characteristics and derive unique therapeutic insights. These are used by BrainQ’s technology to target the recovery of impaired networks.

Artificial Intelligence, Machine Learning

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