Quantifying Human Consciousness With the Help of AI

Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.

Source: CORDIS

New research supported by the EU-funded HBP SGA3 and DoCMA projects is giving scientists new insight into human consciousness.

Led by Korea University and projects’ partner University of Liège (Belgium), the research team has developed an explainable consciousness indicator (ECI) to explore different components of consciousness.

Their findings were published in the journal Nature Communications.

Consciousness can be described as having two components: arousal (i.e. wakefulness) and awareness. The act of opening one’s eyes indicates a state of wakefulness, and the ability to perceive differences or follow commands shows awareness.

Under different conditions—dreamless sleep, sleep with dreams, anesthesia and severe brain injuries—there are different levels of consciousness. Until now, there had been no reported systems of measurement capable of quantifying the two components of consciousness.

A reliable measure of consciousness

The ECI developed by the researchers uses deep learning to distinguish between wakefulness and awareness under physiological, pharmacological and pathological conditions. The team analyzed the brain activity of six healthy participants during sleep; 16 healthy participants under ketamine-, propofol- or xenon-induced anesthesia; and 34 participants who were a coma as a result of severe brain injury.

The study showed that ketamine-induced anesthesia and rapid eye movement sleep (when most of our dreaming happens) with low wakefulness and high awareness are clearly different from other states. What’s more, the parietal regions of the brain were found to be the most relevant for measuring wakefulness and awareness in altered states of consciousness.

The study authors describe the novel tool as a potentially “reliable discriminator and valuable tool as an objective measure of consciousness.”

This paper presents “the world’s first technology to quantify the arousal and awareness at the same time,” states study co-senior author Prof. Seong-Whan Lee of Korea University’s Department of Artificial Intelligence.

The ECI has the potential to improve clinical care for patients in different settings, such as monitoring anesthesia-induced states during surgery and diagnosing patients in comas or vegetative states.

According to co-senior author Dr. Olivia Gosseries, who is co-director of the Coma Science Group at the University of Liège, “future studies are needed to implement this novel indicator in the clinical routine and even to develop an online and real-time tool to be implemented in our hospitals, operating rooms and intensive care units.”

This shows the outline of two heads
Consciousness can be described as having two components: arousal (i.e. wakefulness) and awareness. Image is in the public domain

HBP SGA3 (Human Brain Project Specific Grant Agreement 3) is the last of four phases of the Human Brain Project initiative. “This fruitful international collaboration between HBP partners illustrates the efficacy of the EBRAINS data sharing platform tools for large human neuroimaging sets,” says study co-author and Coma Science Group lead Prof. Steven Laureys from the University of Liège.

“Our work capitalized on the efforts made by the Human Brain Project teams to promote and facilitate digital neuroscience and brain medicine.” The DoCMA (Disorders of Consciousness (DoC): enhancing the transfer of knowledge and professional skills on evidence-based interventions and validated technology for a better management of patients) project ends in January 2023.

Project Links:

HBP SGA3 project website

DoCMA project website

About this deep learning and consciousness research news

Author: Press Office
Source: CORDIS
Contact: Press Office – CORDIS
Image: The image is in the public domain

Original Research: Open access.
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning” by Minji Lee et al. Nature Communications


Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported.

Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness.

We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions.

Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness.

This indicator provides insights into the neural correlates of altered states of consciousness.

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  1. It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with primary consciousness will probably have to come first.

    The thing I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.

    I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.

    My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461

  2. You should also study this topic with a tibetan monk like an example or someone who can became conscious on a frequency state of gama, alpha, and theta by his or her own will and try to distangle the arousal (wakefulness) and awareness (subjective experience) too.

  3. The subject of consciousness ends in semantics, in the object/objectivity of oneself…

    That consciousness beyond semantics, can become oneself in sensation emotion mentation…

    So maybe there is outer consciousness, inner consciousness and self consciousness…
    …all verifiable evidence of oneself, in movement…

    Understanding this could help understanding others, maybe…thanks

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