Are Brain–Computer Interfaces Feasible With Integrated Photonic Chips?

Abstract

The present paper examines the viability of a radically novel idea for brain–computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.

Consciousness and inward electromagnetic field interactions

“…The anesthetic-induced collapse of the attractor certainly fits with an anesthetic-induced collapse of information integration that occurs at loss of consciousness ( Oizumi et al, 2014 ; Sarasso et al, 2015 ; Tononi et al, 2016 ; Eagleman and MacIver, 2018 , 2021 ; Eagleman et al, 2018a , b , 2019 ; Ward and Guevara, 2022 ). A more sophisticated approach would measure both electric and magnetic fields, together with photonic energies ( Salari et al, 2021 ), and combine these into multi-dimensional attractors. Even better would be approaches which allow us to record EMFs from deeper regions of the brain, like the thalamus, midbrain and brainstem, together with cortical level signals.…”

The future is here: Mind control and torture in the digital era

“…Neural implants have multiple applications in medicine, especially related to neurostimulation in motor and sensory disorders, but also epilepsy, and they are in early experimentation stage in depressive and obsessive-compulsive disorders (Costa e Silva & Steffen, 2017). This is a rapidly progressing research area in which biochips and implants are built in new and better materials that produce no tissue rejection, incorporating nanotechnologies to diminish the size and with more powerful software to control and interact with the neural system (Dabbour et al, 2021;Salari et al, 2022;Wan et al, 2021) while, again, there is no international regulation of its use (McGee & Maguire, 2007). The most important concern regarding the use of neuroimplants -not in the near future, for now -is represented by the possibility of controlling an individual's mental functions via wireless waves interacting with the electric activity of the brain.…”

Monitoring Alzheimer’s Disease via Ultraweak Photon Emission

“…Such correlations suggest that we may use UPEs as a correlative signal to monitor different internal states across the stages of ADRD pathology and to expand the clinical criteria, particularly in the preclinical and mild cognitive impairment (MCI) stages where memory loss and other problems are not always evident. Discrimination between the interferometric patterns of normal, and preclinical stages will be non-trivial but tractable via machine learning, based on observation of highly synchronized brain activities with strong UPE correlations for specific cognitive tasks (3). With an analysis of signals over thousands of training trials, it will be possible to obtain an average pattern for feature extraction, enabling pattern recognition directly during preclinical and premarket approval testing.…”

“…Diagnosing AD typically involves a combination of cognitive and memory tests, brain imaging studies, and other assessments performed by a clinician. Based on the observation that the brain spontaneously emits photons, so called ultraweak photon emissions (UPE) (1; 2), we suggest that a brain-computer interface with an integrated photonic chip (BCIPC)(3) may be an efficient real-time method for monitoring early symptoms of AD and related dementias (ADRD). The envisaged technology would support clinicians by providing complementary data to efficiently screen and diagnose AD.…”

“…The envisaged technology would support clinicians by providing complementary data to efficiently screen and diagnose AD. Indeed, We have previously discussed a pattern recognition approach for an efficient interpretation of UPE via the output signals on a photonic interferometer chip (3). Supporting this vision is the fact that various research studies have shown that there are significant UPEs from neurons across the electromagnetic spectrum (1; 4).…”