Brain Computer Interfaces for mental health care
From enabling us to think, feel emotions, control every blink and breathe, the brain is the control centre of us humans and by far the coolest part of our body!
What’s crazy is that our understanding of the brain is minuscule compared to any other system in our body. All the other systems in our body can be mapped — our brain, on the other hand can not (yet!).
However, what is exciting is that there is no telling what could be accomplished by a better understanding of the brain. Treating mental illnesses is one outcome, but certainly not the only one.
Brain Computer Interfaces is a promising future for treating mental illness, understanding our brain and making all our sci-fi dreams come true!
Brain Computer Interfaces (BCI)
All of our bodies’ actions are controlled through neurons as they communicate information to and from the brain. This is possible through the use of chemical and electrical signals. BCI’s is a technology to tap into these signals to control machines. The understanding of how the brain controls movement leads to the development of devices and algorithms that can be implemented on a computer that recognizes these patterns and correspondingly moves artificial advice.
In simple terms brain computer interface is how we communicate from our brain to a device.
This technology can be used to better understand what’s going on in our brain, do crazy things like “installing a language in our brain” or even cure paralysis!
Types of BCI’s
- Non-Invasive BCI
A non-invasive BCI does not directly go in your brain, it sits on the outside of your skull, on top of the skin. EEG’s are common non invasive BCI.
A semi-invasive BCI is placed underneath the skull but it doesn’t penetrate the brain. The most common type of semi-invasive BCI is an EcOg, which is similar to an EEG but placed on the surface of the brain.
Invasive BCIs are able to use micro-electrodes that are placed directly into the cortex and are able to measure the activity of single neurons. These electrodes allow for specific targeting but may only be useful for a few years before brain scarring starts to degrade the signal quality.
When collecting EEG data we have to understand the different frequencies of signals that may be received that indicate different things.
In the brain, we have different frequencies for the different states:
- Gamma Waves (32–100 Hz) — high degree of concentration
- Beta Waves (13–32 Hz) — normal cognitive processing
- Alpha Waves (8–13 Hz) — calm wakefulness
- Theta Waves (4–8 Hz) — sleep or deep meditative state
- Delta Waves (0.5–4 Hz) — high degree of synchronicity indicative of sleep
How we can leverage BCI’s to support mental health/mental illness
So what does this means for mental health….
It’s evident that with the pandemic there has been a rise in mental health concerns and mental illnesses. BCI’s are an opportunity to better support mental wellbeing and detect illnesses as mental health struggles/mental illnesses start primarily with neurons in our brain and body.
For instance when we are stressed our heart rate moves faster and causes changes in chemicals in the brain which modulate cognition and mood, including serotonin. This can be compared with someone who isn’t stressed to determine what “stressed” looks like in someone’s brain.
Similarly we can use this for mental illnesses, by looking at the brain signals of what is shown to be someone with a certain mental illness and what isn’t.
Check out a project I worked on with the team at Neurable on detecting stress levels with the Neurable headset: https://docs.google.com/document/d/e/2PACX-1vSeX6eF-M3YzQXPfJZ9tibAEhV0PhEr0XjDy_GVOURXG9FXhHNhzShM0xkzocYQURTiIFqmRe3mzBBP/pub
Current Applications of BCI’s and mental health care
Technology is the key to providing accessible mental health care and detecting mental illness, as it can provide those struggling with self-administered, digital therapeutics that do not require in-person contact.
Using machine learning to understand the brain signals that are associated with specific mood states
A group of student researchers at University of Southern California are using machine learning to understand the brain signals that are associated with specific mood states. In one of their recent studies, electrodes were implanted directly onto the surface of the brain of patients, and brain activity was recorded over multiple days. Patients’ mood states were assessed using a self-report questionnaire where an algorithm would predict there scores based on recorded brain activity to essentially understand emotion. A similar experiment was conducted with youth on social media, on how their social media feed shaped how they are feeling by tracking brain activity.
Monitoring or correcting abnormal brain signalling of seizures, ADHD and other mental illnesses
Over the past few years researchers across the world have been using Brain Computer Interfaces to monitor and prevent mental illnesses. For example, BCI’s were used to send and receive signals and provide real-time detection and prediction of seizures. Similarly with ADHD the brain signals of someone with or without ADHD to determine if someone has ADHD.
Making wellbeing care more effective
Muse created a headband that uses brain-sensing technology to teach mindfulness. Sensors in the headband measure brainwaves, while a soundscape playing through the app provides feedback on the wearer’s mental state in real time. Users have found that muse has directly impacted their wellbeing.
When it comes to the future of mental health care BCI’s propose an exciting future to detect mental illnesses and provide better mental wellbeing supports.
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