BrainQ’s device uses non-invasive, frequency-tuned extremely low frequency and low intensity electromagnetic fields (ELF-EMF) with aim of promoting neurological recovery in the central nervous system (CNS). The artificial intelligence (AI)-powered device tailors the electromagnetic field characteristics to suit each patient.
Read more to learn why we’re using EMF, the scientific basis behind it, and how AI is applied to optimize its potential.
The brain is organized into neural networks1 which behave in synchrony when performing specific functions, creating measurable neural oscillations, or brain waves, in regular patterns2. These patterns can be detected and measured using electrophysiology tools (e.g., EEG, EMG, MEG). Scientists can now study these patterns and begin to distinguish specific neural networks3–5 and functions. In the case of stroke, or other trauma/disease states, damage to neural networks interferes with neural activity and connectivity6–9, resulting in disability such as impaired motor function. A successful treatment needs to both target a particular network as well as deliver the patterns and frequencies of EMF needed to facilitate its recovery.
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 noninvasive 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.
Treatments for neurological disorders seek to promote and support recovery processes17–19. A number of effects in physiological, behavioral, and functional outcomes have been identified in tissues and organisms exposed to EMF, and these changes are implicated as the mechanisms which likely underlie the observed recovery from BrainQ’s investigational treatment. A number of mechanisms distinguish themselves as candidates which are likely to be causal in mediating the beneficial effects of the field. There is experimental evidence supporting the effect of ELF-EMF on processes specific to recovery from neurological conditions. There is evidence of:
An illustration of neuroplasticity
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.
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