Evaluating the Intrinsic Electromagnetic Field Generated by Neurons From Repetitive Motor Activities in Humans With a Non-contact Non-invasive Electromagnetic Helmet

Introduction The actions of neurons are dependent on electrochemical signal pathways mediated by neurotransmitters and create measurable electrical charges. These charges have been found to be measurable through neuroimaging technologies and now through a novel non-contact non-invasive sensor without supercooling. Identifying whether this technology can be appropriately interpreted with synchronized motor well-defined activities in vivo may allow for further clinical applications. Methods A non-contact, non-invasive helmet constructed and modified using shielding technology with proprietary magnetic field sensors was utilized to measure the brain’s electromagnetic field (EMF). Human volunteers donned helmets and were asked to perform repetitive tapping exercises in order to identify waves consistent with tapping from the left and right hemispheres. A gyroscope was utilized to ensure that measured waves were not from micro-movement but were from neuronal firing. Multiple individuals were tested to evaluate the reproducibility of tapping and commonalities between individuals Results Right and left-sided tapping generated discernible wave changes from baseline measurements obtained by the helmet without a subject as well as differed from when the subject was at rest. Wave patterns varied from person to person but were overall similar in each subject individually. Shielding was necessary to identify signals but EMF was identified when shielding was transitioned from around the helmet to within the helmet design. Conclusion It is possible to measure in-vivo electromagnetic fields generated by the human brain generated by stereotyped tasks in a non-contact non-invasive manner. These waves were reliably obtained within each individual with some variability in morphology from subject to subject however were similar in each subject. Signals varied based on activity and stereotyped motor activities were identified. A helmet using shielding technology within the helmet itself was able to effectively identify EMF signals. Future analysis may focus on translating these waves into functional mapping for clinical applications.