ClearMind: Synthetic telepathy for cognitive enhancement

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This article was written by John LaRocco, PhD.

Introduction

Direct brain-to-brain communication has resulted in enhanced cognitive performance. Invasive, implanted devices have successfully demonstrated this in animals, while non-invasive testing demonstrated similar results for humans (Pais-Vieira, Chiuffa, Lebedev, Yadav, & Nicolelis, 2015; Rao, et al., 2014). While transcranial magnetic stimulation (TMS) was used for initial brain-to-brain interface in humans, transcranial focused ultrasound (TFUS) offers a smaller, more efficient alternative (Danilov & Kublanov, 2014; Sassaroli & Vykhodtseva, 2016).

When combined with electroencephalography (EEG), TFUS has been successfully used in non-invasive brain-to-brain interface (BBI) in humans (Lee, et al., 2018). As consumer EEG-based BCI devices have been available for decades, a low-cost, reliable TFUS device, and BBI protocol, would close the loop in ensuring reliable, entry-level synthetic telepathy. The commercial potential for such a device is massive, as are the wider implications (Pais-Vieira, Chiuffa, Lebedev, Yadav, & Nicolelis, 2015).

Background

Prior forms of neurostimulation were limited by a lack of precision, reliance on cumbersome machinery, or the need for invasive implantation (Danilov & Kublanov, 2014). For example, deep-brain stimulation (DBS) for patients with Parkinson’s disease required a highly invasive electrode to be implanted in the skull. Transcranial electrical stimulation (TES) techniques, including transcranial direct current stimulation (TDCS) and transcranial alternating current stimulation (TACS), lack the precision required for accurate neuromodulation (George & Aston-Jones, 2010). Transcranial magnetic stimulation (TMS) was non-invasive and precise, but required complex machinery. In contrast, TFUS requires smaller components, is non-invasive, and relatively precise (Ye, Brown, & Pauly, 2016). Although the mechanism behind TFUS is not entirely known, BBIs have been demonstrated repeatedly (Rao, et al., 2014). Research even suggested that BBIs may greatly enhance problem-solving speed (Pais-Vieira, Chiuffa, Lebedev, Yadav, & Nicolelis, 2015). The simplest devices are EEG-based BCIs with a wireless connection, while the most invasive are physically wired brains (Lebedev & Nicolelis, 2017). The lack of a precise, non-invasive stimulation system limited the prior potential of potential BBI systems. Similarly, a protocol allowing the secure transmission of stimulation parameters is required.

Implementation

The ClearMind would utilize a consumer-grade EEG headset, the OpenBCI Ultracortex Mark IV, with ultrasound transducers slotted into the headset, in spring-loaded pegs. The Ultracortex provides a rigid helmet or frame to fix ultrasound transducers in position. The specific stimulation intensity should be kept beneath international levels to ensure individual safety (Yoo, 2018). Based on the European standards, the threshold would be Mechanical index < 1.9, Isppa < 12 W/cm2, and Ispta ≤ 3 W/cm2 (Lee, et al., 2017). The transducer positioning should enable stimulation of the motor cortex, optic nerve, or other areas requiring stimulation (King, Brown, Newsome, & Pauly, 2013). The sampling rate would be at 200 Hz, owing to the frequency bands commonly used EEG-based BCI (Volosyak, Guger, & Gräser, 2010). The use of an existing commercial EEG headset, the 4-channel OpenBCI Ganglion, able to send information online would greatly accelerate development. Dry electrodes would be prioritized, as they do not require conductive gel. BCI paradigms requiring little to no external stimuli, such as covert speech and motor imagery, would be the default setting (Volosyak, Guger, & Gräser, 2010). Transmission to a computer or smartphone, for receiving and sending commands to other devices, will be conducted over encrypted MQTT messages. More specialized paradigms and transducer arrangements, such as medical stimulation for medical or rehabilitative purposes, could be implemented in over time (King, Brown, Newsome, & Pauly, 2013).

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Figure 1: System module architecture

Significance

The availability of even a single transducer-based component for the OpenBCI ecosystem would make non-invasive BBIs available to the general population, and to innumerable commercial sectors. It would directly benefit the physically disabled and impaired, by enabling an intuitive feedback system to external devices and caregivers. Professions requiring constant communication and attentiveness, such as in transportation, e-sports, aerospace, finance, and security, would clearly benefit. The range of medical benefits is similarly impressive, as with applications for pharmaceutical delivery, chronic pain treatment, stress reduction, depression treatment, Parkinson’s disease, and cognitive enhancement. The system could form the basis for a truly global cognitive system, especially if combined with social media and analytics.

References

Danilov, Y. P., & Kublanov, V. S. (2014). Emerging Noninvasive Neurostimulation Technologies: CN-NINM and SYMPATOCORECTION. Journal of Behavioral and Brain Science, 2014.

George, M. S., & Aston-Jones, G. (2010). Noninvasive techniques for probing neurocircuitry and treating illness: vagus nerve stimulation (VNS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). Neuropsychopharmacology, 35(1), 301-316.

King, R. L., Brown, J. R., Newsome, W. T., & Pauly, K. B. (2013). Effective parameters for ultrasound-induced in vivo neurostimulation. Ultrasound in medicine & biology, 39(2), 312-331.

Lebedev, M. A., & Nicolelis, M. A. (2017). Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiological reviews, 97(2), 767-837.

Lee, W., Croce, P., Margolin, R. W., Cammalleri, A., Yoon, K., & Yoo, S. S. (2018). Transcranial focused ultrasound stimulation of motor cortical areas in freely-moving awake rats. BMC neuroscience, 19(1), 57.

Lee, W., Kim, S., Kim, B., Lee, C., Chung, Y. A., Kim, L., & Yoo, S. S. (2017). Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface. PloS one, 12(6), e0178476.

Pais-Vieira, M., Chiuffa, G., Lebedev, M., Yadav, A., & Nicolelis, M. A. (2015). Building an organic computing device with multiple interconnected brains. Scientific reports, 5, 11869.

Rao, R. P., Stocco, A., Bryan, M., Sarma, D., Youngquist, T. M., Wu, J., & Prat, C. S. (2014). A direct brain-to-brain interface in humans. PloS one, 9(11), e111332.

Sassaroli, E., & Vykhodtseva, N. (2016). Acoustic neuromodulation from a basic science prospective. J Ther Ultrasound, 4(1), 1.

Volosyak, I., Guger, C., & Gräser, A. (2010). Toward BCI Wizard-best BCI approach for each user. Transactions on the Annual International Conference of the IEEE Engineering in Medicine and Biology, 4201-4204.

Ye, P. P., Brown, J. R., & Pauly, K. B. (2016). Frequency Dependence of Ultrasound Neurostimulation in the Mouse Brain. Ultrasound in medicine & biology.

Yoo, S. S. (2018). Technical Review and Perspectives of Transcranial Focused Ultrasound Brain Stimulation for Neurorehabilitation. Brain & Neurorehabilitation, 11(2).