In the real world the technology to read and interpret the human brain has been arround for almost a century now and the idea of controlling devices with your brain has been around for around half a of that
Back when programmers were just starting to write code on computers and not on paper cards, people already knew it was possible to control something with your mind.
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)
In 1969 the operant conditioning studies of Fetz and colleagues, at the Regional Primate Research Center and Department of Physiology and Biophysics, University of Washington School of Medicine in Seattle, showed for the first time that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity. Similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity.
I remember in the news back in 2008 or so, some kids playing ping pong with a ball controlled by their brain and things like that have been around since 1990, nowadays one can play games like world of warcraft or counter strike and any other game using brain electronics.
What does it mean
That mind control not only was already a fantastical idea back in the years of mythology but the technology required to make it possible isn't that advanced.
nowadays people can make giant computers in minecraft using 2-3 active devices, for control, input and output. One can effectivelly be playing another computer game inside a computer created in minecraft...
The same thing can be mimicked with a giant medieval computer made of wood and metal. (more on that on another question)
What I desire
-Technology level : scandivian europe first millenia (after christ)
-Earth like planet
-Fantasy, low magic
-European medieval solarcore culture
-Fantasy tropes of early and pre medieval mythology are real, going from the greek griffons and romanian strigoi to the nordic elves and dwarves or the roman cacus and everything inbetween
Those born without magic can learn the language of magic runes, encrypting this language onto objects gives them functions with specified responses based on specific inputs.
The artificial mages learned that they can mimic fire magic by encrypting stones with functions that make them heat surprisingly fast one activated with the desired method.... like when being thrown they will instantly heat up to a specified temperature and consume the energy of the caster to create oily substances...
turning a stone with some writings on it into a powerful re-usable molotov, they can also make other things like self constructing tools that active at will, and pretty much anything they can encode as long as the artificial mage has enough energy.
Ok now that we know how runes work.
I want drones that fly around the Artificial Mage, they keep afloat by spinning in the air. The drones are a swarm, controlled by the brain of the artificial mage, they can be used for self defense, mobility, to scout around or to move objects around like amazon drones.
I want to know what method would enable the user to control as many drones as possible.
Control requires the user to know and decide the tridimensional position,vector and rotation of each drone, if the user has like 1000 drones flying around but can't answer to the question ''what exactly is drone C-33 doing in this moment?" then it means they don't actually control the drones
More research(on the capacity of the brain to process stuff compared to computers)
On the other hand, the brain has about 1e15 synapses, making it analogous to five million contemporary 200 million transistor chip "cores". link
How many GHz is a human brain? Comparing computer and brain frequencies, Bostrom notes that “biological neurons operate at a peak speed of about 200 Hz, a full seven orders of magnitude slower than a modern microprocessor (∼2 GHz).”6 It is important to note that clock speed, alone, does not fully characterize the performance of a processor. link
Research on multi-tasking for controlling and doing many things at once
One way we can examine the effects of multitasking on behavior and the demands it places on relevant brain networks is by analyzing “task switch costs.” A switch costis a reduction in performance accuracy or speed that results from shifting between tasks. Arich body of research in psychological science has documented that the behavioral costs of task switching are typically unavoidable: individuals almost always take longer to complete a task and do so with more errors when switching between tasks than when they stay with one task. Neuroimaging work from our lab and others has helped to highlight the effects of task switching in the brain. In one functional magnetic resonance imaging (fMRI) study, we had subjects classify stimuli on one of three dimensions (color, shape, or pattern). In terms of behavior, one finding was that subjects took longer to classify stimuli in switch trials (i.e., where the task had changed from the previous trial) compared to stay trials (i.e., where the task stayed the same). In terms of the brain, we found that frontoparietal regions––including those of the frontoparietal control and dorsal attention networks––were more responsive during switch than stay trials. In fact, consistent with the view that multitasking creates heightened neurocognitive demands, the strength of task representation in the control network was greater when subjects switched to a new task than when they stayed with the same task. This means that when we switch from one task to another, it requires more neural processing because we have to bring back to mind the new task’s representation and then use it to allocate attention to information that is relevant to perform the new task. As a consequence, when we switch between tasks, we lose the benefits of automaticity and efficiency that come from staying focused on a single task. Studies from other labs have reached similar conclusions. One fMRI studyexamined the effects of switching between tasks: subjects performed a single task repeatedly or two tasks intermixed in a block of trials. Response times were slower during task-switch blocks, and brain patterns reflected this effect. Nodes of the frontoparietal control network and dorsal attention network were more active during switch blocks, revealing increased neurocognitive demands associated with switching. From developmental studies, we have learned that older individuals often exhibit reduced abilities to selectively attend to and engage cognitive control in support of goal-directed behavior. Age-related fMRI studies provide initial hints as to what neural changes make multitasking (or task switching) particularly challenging for older adults. In one, older adults’ diminished multitasking ability was associated with reduced connectivity between brain networks of attention, control, and memory, compared to young adults. Psychological science and neuroscience indicate that our minds are taxed by multitasking. When we attempt it, we must engage in task switching, placing increased demands on neurocognitive systems that support control and sustained attention. While engaging these systems can partially mitigate its behavioral costs, multitasking is not free––we pay a price in increased demands on these systems and some performance deficit typically occurs. A Spotlight on Media Multitasking With the explosion of digital media and the commodification of our attention (referred to as the “attention economy”), “media multitasking” has become ubiquitous. Have you ever opened your laptop to check your email or complete a work assignment, and put on Spotify or Netflix in the background? This kind of multitasking––engaging with or switching between multiple media streams––has attracted considerable interest given behavioral trends. We know that American youth spend an average of 7.5 hours a day with various media and at least 29 percent of that time involves media multitasking. Data from other countries show a similar pattern, and the phenomenon extends to adults. In 2009, Cliff Nass’s lab at Stanford developed what has become a widely used index––the Media Multitasking Inventory (MMI)––to quantify the extent to which an individual engages in this practice. The original MMI asked individuals to report their hours of media consumption for each of 12 different media categories (television, music, text messaging, and so forth), along with the extent to which when engaged with one medium they were also engaged with each of the others. Test-retest reliability of MMI is high over a week (r = .93) and moderate over a one-year period (r =.52), and shorter versions and different variants have been developed. The MMI score from the Nass lab represents the mean number of media with which an individual multitasks during a typical consumption hour. A high MMI score means an individual engages in a lot of media multitasking (e.g., checking email while also perusing Facebook and watching Netflix), and a low score means he or she does not (e.g., checking email without any secondary media). In the 2009 study, heavier and lighter media multitaskers were asked to perform a set of cognitive tasks that place demands on attention, control, and memory. This study initiated a rapidly evolving literature that seeks to answer the fundamental question: does media multitasking in everyday life impact our minds and brains, affecting performance even when we are single tasking?