Brain-Computer Interfaces Changing the Future of Dreams.
Lucid dreaming.
“We spend one-third of our life sleeping!”… “Sleeping is such a waste of time!”. I’m sure you’ve heard both of these statements by now, and probably more than once. Although sleeping is crucial to life, they’re not entirely wrong. Society has come to the point where we simply don’t have enough hours in the day to do all that we desire, whether it is getting that extra relaxation time or revising for a test. Wouldn’t it be great if we could accomplish all of these tasks during our designated sleep time?
By now you must be familiar with the ambition of controlling your own dreams, being able to go to sleep and dream about whatever you want! Imagine having the power to live a whole other reality during the night.
What if I told you that all of this could soon become our reality? No… this isn’t some matrix magic where you take a pill or can connect your brain to the cloud. This is called lucid dreaming, a phenomenon where the sleeper is aware that they are in a dream and with sufficient practice can control the contents of the dream. This level of lucid dreaming “skill” is hard to achieve, and only 20% of people experience a lucid dream once a month, let alone understand how to control the dream.
The study of lucid dreaming has incredible potential for dream science research. Through the use of brain-computer interfaces (BCI) applied to lucid dreams scientists have been able to discover incredible things! Such as communication from the dreamer to the experimenter, helping patients with trauma, and even being able to induce lucid dreams. The more we understand dreams, and the more we can experiment with them, the faster we will be able to harness sleeping time productively.
A lot right? I know… let’s backtrack.
What are Brain-Computer Interfaces?
A BCI is a mechanism that collects signals from the brain and connects them to a computer. The mechanism translates brain signals into actions by identifying the desired input and connecting it to the right output. The signals can be measured using various methods, but we will focus on electroencephalogram (EEG).
How does EEG work?
EEG is a non-invasive method that records the electrical activity from the surface of the scalp. Small metal discs (sensors) called electrodes are placed on certain areas of the head. When neurons communicate with each other electrical signals are transferred. These electrical currents reach the scalp and the EEG records the voltage difference between them. For EEG to work there has to be a minimum of two electrodes that are recorded simultaneously. EEG is the preferred method for brain imaging because it does not need any implantations (non-invasive), is easy to transport, and is reasonably cheaper compared to the other types of brain imaging.
How are brain signals transferred to an action?
There are quite a few steps scientists need to complete before being able to run a BCI successfully. Let’s imagine you were aiming to build a BCI that involved pushing a virtual block around a screen (using only your thoughts). First, you need to physically move the block (using your cursor) and allow the EEG to map your brain waves each time you move the block in a different direction. Now you can assign the different brain waves to an output. The next time you try to move the block you should be able to do it just using your mind! The computer will interface your brain signals of thinking about moving the block to your brain signals of you actually moving the block. Well done; you’ve now effectively linked your thoughts to a command! That was very easy, right? (No, it was not…)
Neuroscientists have accomplished and/or theorized about astonishing things when integrating BCI into the field of dreams! Four of those accomplishments will be covered in this article:
Inducing Lucid Dreams.
As already mentioned, only a small fraction of people genuinely experience lucid dreams. Even a smaller part of that fraction can control the contents of their dreams. The “iBand”, an EEG brain-sensing headband can help all of us achieve lucidity. Most dreams occur during the fourth stage of sleep, REM (rapid eye movement). The BCI mechanism can detect when the sleeper is in the REM stage from the different frequencies detected from the brain.
During the REM stage, the I-Band performs a series of flashing colored lights and audio to remind the dreamer that they are in a dream. The prompts are designed to reach the patient through the dream and induce lucidity. The dreamer gets hit with some sort of ‘reality check’ to help them break the narrative of the dream. There are many other BCI technologies that have been developed to identify the REM stage and induce lucidity. They all work by examining the dreamer’s brain waves and sending messages to alert them to the fact that they are dreaming.
Sensing emotional states in the dreamer.
BCI is capable of identifying emotional states in a person. EEG sensors help determine the slightest mood changes in the brain from a variety of emotions. An investigation written in “EEG Based Emotion Classification Mechanism in BCI” analyzed the impact of positive and negative emotions. The study was able to successfully identify calm, anger, and happiness. To be able to identify the emotions, the mechanism had to be able to assign the different brain signals to the emotions. An emotion classification system that differentiates happy and unhappy emotions using classical music and pictures as a stimulus was put in place.
Emotional recognition through BCI has been performed on subjects who are awake, but what would it look like on sleeping subjects? The mechanism and analysis of the signals would be the same if not a little modified due to sleep. However, there wouldn’t need to be a stimulus because the dreams would serve as a stimulus. Emotional recognition of a dreamer could be used to interpret their dreams and dreaming patterns, and could even help patients recover from recurring nightmares.
Overcoming recurring nightmares.
The root of most recurring nightmares is traumatic past events, insecurities, anxiety, lack of sleep, and/or an irregular sleep routine. Recurring nightmares are most common in people with PTSD (Post-traumatic stress disorder).
“Lucid dreaming can be a great tool to help people overcome nightmares.“ — Dr. Ursula Voss, Goethe University.
An approach to this is called regression therapy, the goal is identifying and understanding the past events that cause a negative impact. Lucid dreaming can allow the patients to relive and ‘reinvent’ the bad experience.
There are visions and methods theorized for helping people overcome nightmares using BCI but none have been successfully performed yet. Even though the area of dreams and BCI is undeveloped compared to other BCI subcategories this can definitely be developed.
We have already established that BCI can:
- Induce lucid dreams.
- Monitor emotions.
So putting two and two together, BCI should be able to help prevent recurring nightmares. Although not yet achieved, I believe it could be done by following these steps:
Step one (planning): The patient would have to discuss with a professional the dreams that have been “haunting” them. For certain traumatic events, it is important to relive the moment again in order to find closure. The decision on what stimulus (through a BCI mechanism) the patient would experience would need to be made based on the contents of the recurring nightmare.
Step two (reliving traumatic events): Commercial BCI devices like the “I-Band” can help patients experience lucid dreams, but they can also affect the contents of those dreams. The device can play pre-recorded sounds and flash different lights to help the patient instigate the dream and the desired moment they are seeking. A 48-hour hackathon experiment “Towards a Passive BCI to Induce Lucid Dream”, shows that visual prompts can influence the contents of the dream. The experiment used an EEG cap to measure the brain signals and a flashing blue LED night mask to induce a lucid dream. One of the patients reported having seen blue lights as the main content of his dream as he was standing behind an aquarium. Now, imagine the possibilities of combining a visual with an auditory stimulus (and perhaps even a physical stimulus)! The sleepers could dream about their nightmare/past event thanks to the use of incentives that reminds them of the situation.
Step three (inside the dream): Using emotion-sensing mechanisms the BCI would increase/decrease (or even change) the stimulus based on the patient's emotional responses. Different people might be facing different negative emotions they want to get rid of inside their nightmares. For instance, someone’s goal could be to stop feeling the recurring (negative) angry emotion and instead be in a neutral state. When the BCI is detecting the brain waves of negative angry emotion, it could provide a positive catalyst (pre-planned) until the brain waves show positive emotion. Once the positive emotion is indicated, it could mean that the sleeper turned the nightmare into something bright.
Limitations:
- The mechanism and programming in the BCI would be challenging to create. The BCI would first need to be programmed to detect lucid dreams and induce them during REM. Next, it would need to be able to differentiate emotions from each other. Finally, it would have to be programmed to apply different stimuli based on the emotion the patient is feeling.
- The solution I theorized was implementing positive stimuli until the brain waves show positive emotions. But it is possible that the brain would show happy emotions because the stimulus prompted it to enter a new dream and leave the nightmare unresolved. Pause… Does leaving the nightmare mean it was resolved? What if the nightmares come back when the patient isn’t monitored? The sleeper could be trained to ‘leave’ the recurring nightmare with the help of BCI. But then again, how much time would this process require?
This topic raises a ton of questions that cannot be answered today, next week, or next month…. But perhaps in six months? Who is to say that the next advancement in BCI won’t involve putting an end to recurring nightmares?
Communication from dreamer to the real world.
Before BCI dreamers could only communicate from lucid dreams using their eyes (left to right movements) or clenching their fists into morse code. Now, this has changed. Think back to the very beginning of this article (not too long ago!) when you learned how thoughts are transferred to action. The example I used was moving a virtual block on a computer using your thoughts, nothing too crazy right? How about moving a virtual block on a real computer from your dream?
This almost sounds like communication from two different realities. Or communication from inside your consciousness to a real-world computer.
Moving a virtual block from within a lucid dream.
An Emotiv EPOC+ (EEG technology) headset developed for determining the presence or absence of a mental task was used in “A pilot investigation into brain-computer interface use during a lucid dream”. Additionally, the study used already experienced lucid dreamers. The mental task that the BCI identified was titled “mental block pushing task” (MBPT), where the virtual block on the screen was being pushed forward.
The process is similar to the explanation at the beginning of the article:
- The virtual block is presented on a screen.
- EEG collects data on brain signals of the neutral (resting) brains of each of the patients. Not performing the MBPT.
- EEG collects the data of the patient physically performing the MBPT (using a cursor).
4. Once the brainwaves are mapped to actions, the subject can imagine the block pushing and the BCI system will move the block. This works quite easily and with reasonable precision as there are only two possible outputs with varied brain waves: resting (passive) and performing the MBPT. The subject (awake) would train to move the block until they could move it with high accuracy.
5. Once the subject is lucid, they can perform the task in their dream. The EPOC+ will detect the brain waves of thinking about moving the block and move the block on a real computer. The brain treats dreamed signals similarly to waked signals.
Okay… but seriously how does this work? What exactly are the lucid dreamers doing in their dream to evoke brain signals? The author of the article “I can control a computer with my mind - from inside a dream” did a similar experiment and explained exactly what she saw in her dream. When she became lucid, she was in the room where she fell asleep. She could go to the computer and do the task. This is one of the possibilities that the subjects in the previous study could have done. Perhaps, they could have simply imagined moving the block as they were dreaming and the EEG would pick up similar signals of the MBPT.
Can communication be developed?
Although the movement of the virtual block from the dreamer to reality is some sort of communication, it isn’t necessarily communicating anything. This is because there was one input and one output, not much can be done with these limitations. As specified in the research paper the number of outputs can be increased to allow for proper communication.
BCIs created the possibility of typing using only the brain, but could this be implemented to sleep? Possibly, but with a different mechanism. In most BCI typing scenarios the BCI distinguishes the letters from one another based on the flashing lights assigned to each letter. This way when the subject is looking at a letter, the BCI can pick up the different signals due to the diverse frequencies. As shown below:
Merely just imaging letters does not work as the brain waves produced are fairly similar. On top of that many letters look alike to our brains and computers, such as “V” and “U”. The system shown in the video also wouldn’t work during sleep, the brain isn’t capable of replicating all of the flashing light frequencies in our dreams and let alone have the BCI detect which one we are imagining.
However, other more straightforward methods of communication are possible. Such as shifting a block left (for no) and right (for yes). Or install a few different frequency flashing lights and include them in the sleeping mask, this way the dreamer could still sense the frequencies with their eyes closed.
To actually be able to classify this as ‘communication’ it has to go both ways. Lucid dreamers can hear external audio when they are dreaming, and they can hear people talking to them when they are in their dream. With the help of BCI, they can respond with a command or a yes/no signal. When BCI is further developed, communication through methods such as typing could be possible.
What does the future hold?
Let’s take a step back. A field as diverse as BCI is going to be developed outside of the medical spectrum. Remember when I told you that BCI wasn’t some matrix magic or connecting yourself to the cloud? Well, right now it sure isn’t, but in the future (coming faster than we could possibly imagine) it might just be our new reality.
One of the leading entrepreneurs Elon Musk and his company Neuralink is inching towards that goal. A “whole brain interface” or as Musk calls it a “digital tertiary layer.” This whole brain interface would be an interface so smooth and so compatible with our existing brain that it would feel like a part of us. This interface would allow us to wirelessly connect with phones, the web, the cloud, computers, and even with people with the same interface in their heads. The process would feel smooth, and natural, so similar to the thinking that already goes on in our heads.
This is a lot to take in… I know. It does kind of sound like something from the matrix.
You may not believe it’s possible, and you’re not wrong. It’s not possible today but this era of whole-brain interfaces is approaching FASTER than you think.
With the development of the whole brain interface the people who complain about sleeping for 1/3 of their life would have their “problem” solved! They could sleep while: being productive, doing work, or simply dreaming about whatever they want. Am I starting to sound a little bit mad? To some people, I definitely am. It comes as no surprise that the future of BCI is an area of major controversy, and rightfully so. Neurotechnology should always be regulated, especially when it’s used with bad intentions.
As always, laws are put into place now to ensure that neurotechnology is used for good:
Five human and neurotech rights:
- Right to mental privacy.
- Right to own identity.
- Right to own decision-making.
- Equal access to mental augmentation (to prevent certain people from becoming “superhumans”).
- Protection against bias in artificial intelligence.
To finish the article on a positive note, a lot of time will go into perfecting whole brain interfaces before they are released to the public. The future of BCI offers many more positive aspects than negatives. BCI technology has already helped countless patients with paralysis, communication, strokes, and many other medical threats!
Thank you for sticking around long enough to finish the article! I hope to see you again soon! Consider this as a little “trailer” for what the future of dreams (and BCI in general) could look like…
Works Cited.
Buyting, Sonya. “Can We Control Computers with Our Dreams? | CBC Radio.” CBCnews, CBC/Radio Canada, 1 May 2020, https://www.cbc.ca/radio/quirks/don-t-pee-in-the-pool-bees-play-ball-and-lucid-dreaming-1.4008562/can-we-control-computers-with-our-dreams-1.4008629.
Carr, Michelle. “I Can Control a Computer with My Mind — from inside a Dream.” New Scientist, New Scientist, 4 Aug. 2017, https://www.newscientist.com/article/2120995-i-can-control-a-computer-with-my-mind-from-inside-a-dream/.
Hamone, Morgane, et al. “Towards a Passive BCI to Induce Lucid Dream — Researchgate.net.” Research Gate, Apr. 2019, https://www.researchgate.net/publication/332750379_Towards_a_Passive_BCI_to_Induce_Lucid_Dream.
Kaur, Barjinder, et al. “EEG Based Emotion Classification Mechanism in BCI.” Procedia Computer Science, Elsevier, 8 June 2018, https://www.sciencedirect.com/science/article/pii/S1877050918308196.
Kyamakya, Kyandoghere, et al. “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies.” Sensors, vol. 21, no. 7, Mar. 2021, p. 2273. Crossref, https://doi.org/10.3390/s21072273. (Figure five)
Mallett, Remington. “A Pilot Investigation into Brain-Computer Interface Use during A Lucid Dream.” International Journal of Dream Research, Apr. 2020, https://journals.ub.uni-heidelberg.de/index.php/IJoDR/article/view/68010.
Urban, Tim. “Neuralink and the Brain’s Magical Future.” Wait But Why, 26 Jan. 2022, https://waitbutwhy.com/2017/04/neuralink.html#part5.
Yamabe, M., Horie, K., Shiokawa, H. et al. MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks. Sci Rep 9, 15793 (2019). https://doi.org/10.1038/s41598-019-51269-8 (Figure four)
