Projects
Classes
About
CV
 
 


 
 

 

produceconsumerobot


 
 


 


 
 
 
 
Thinking Cap

 A hat that measures the wearer's brain activity from the wearer's scalp EEG and projects changes in neuronal synchrony on modular light arrays. Highly focused attention increases the brain's beta and gamma oscillations and turns the frontal light modules red, while entering a meditative state produces large alpha rhythms and turns the rear light modules red.
 		  
 
 

Also see my other biofeedback wearables / wearable computing accessories.
 

 
Tutorial: How to Measure Your Brainwaves (EEG)

The following materials were prepared for a studio (workshop) "Measuring Biological Signals: Concepts and Practice" presented at the Tangible and Embedded Embodied Interactions conference, MIT Media Lab, Boston, MA.
 
Background:
There are around 100 billion neurons in the brain, which is approximately the number of galaxies in the universe. To prevent total chaos, the brain uses rhythmic syncopation at several timescales so that local networks of neurons can synchronize with one another and with distant networks to orchestrate elaborate behaviors. Basically, the idea is that when neurons fire in synchrony with one another, they are better able to communicate than when they fire out of sync.
  The rhythmic synchrony of electrical impulses in the brain can be recorded as microvolt oscillations on the scalp (white trace) and this is called the Electroencephalograph (EEG). Different behaviors lead specific brain areas to synchronize at different frequencies. For example relaxation or meditation with your eyes closed leads to increased alpha (8-12Hz) waves over the visual cortex (rear of the head).
On the other hand beta (14-30Hz) oscillations are associated with active thinking and concentration, while gamma (30-50Hz) oscillations are believed to reflect local neural processing, commonly activated during focused attention.
 
It should be noted that these statements are wild simplifications of what's actually going on in the brain. This figure is from one of my publications in the Journal of Neuroscience and shows activity recorded from 96 electrodes regularly spaced over the hippocampus, a brain area involved in learning and memory. Notice how during active waking there is greater high frequency activity on electrodes placed in more dorsal (upper) anatomical layers, while during REM sleep there is greater oscillatory activity on the electrodes in more ventral (lower) layers. This change between active waking and REM sleep may reflect important changes in the mnemonic processing of different hippocampal subregions.
 
Electrodes:
I'm currently using "wet" electrodes that have a sponge material surrounding the electrode which you must keep wet with a saline gel (salt and Johnson's baby shampoo works well). I got these electrodes from a friend in a neuroscience lab neighboring my PhD lab (sorry no direct links, but you can find information on the modeeg site, or by searching for "EEG electrode" in google).
There are ways to make "dry" electrodes with active buffering circuitry -
http://openeeg.sourceforge.net/doc/hw/joe_ae/
http://uazu.net/eeg/ae.html
http://www.dcc.uchile.cl/~peortega/ae/
and this is how the Neurosky headset works, but I've had limited success in my own attempts.

The Thinking Cap uses 4 electrodes, a ground over the frontal lobe, a reference in the middle of the occipital cortex, and two recording electrodes bilaterally placed over the right and left occipital cortex.
 
Amplification:
Since scalp EEG is on the order of microvolts, you need to amplify the signal by a factor of ~10K-100K.

The 2 electrodes (plus GND & Ref) on the thinking cap feed into an openeeg modeeg amplifier (which can be purchased at olimex). I modified it slightly to increase the max gain and I made a voltage regulator/splitter circuit to replace the standard digital board.

 
 

Alternatives:
An alternative to finding electrodes and doing all the amplification DIY-style, it's now possible to purchase a single channel off-the-shelf headset from Neurosky which sends the raw EEG via bluetooth. In addition to potentially being easier to set up and using dry electrodes, this system also ensures user safety by removing the user from any possible shock hazards associated with being attached to wall power. The major drawback is that it has only a single channel, but maybe this will improve in the next version.

Emotiv has a headset with many channels, but they are currently not as DIY friendly due to the fact they will not allow users access to raw EEG data, but only to their processed data. Maybe we need some pressure from the DIY community here or someone to reverse engineer their processor.
 
Analysis:
After amplification, I read the 2 EEG signals separately into two dspics (dspic30f3013)
I use a passive low-pass filter on the incoming signal (1uF capacitor + 3K3 Ohm resistor = cut at ~48Hz) to reduce 60Hz noise and higher frequencies that I won't analyze.
 
Here is a a slightly modified version of this circuit on a breadboard.

This circuit reads the incoming brain signal and uses an FFT to separate out the power (amplitude) in 3 specified ranges: Alpha (8-12Hz), Beta (14-30Hz), and Gamma (30-50Hz). The changes in power of each frequency band are displayed on 3 RGB LEDs so that increases in power turn the LED red, decreases in power turn the LED blue, while power levels at the averaged baseline appear in the green range.
Below is a pinout of the dspic30F3013 and here is the full datasheet.
 
What is an FFT?
Basically, the FFT (fast Fourier transform) is a method of decomposing a signal into its different frequency components. Technically, it's a change of basis from a time-domain into a new basis consisting of sine and cosine waves of different amplitudes and phases.
A classic example is a square wave. Take a square wave of some arbitrary frequency, say 1 Hz. You can approximate this square wave with a single sine wave at 1Hz and capture much of the wave, but leave a lot left to the imagination. But, if you add another sine wave at 3Hz (of a lower amplitude) you can start to fill in those sharp peaks. Add another sine wave of 5Hz... etc... now we're starting to see something that looks like a square wave.

In a similar manner you can decompose any signal into it's frequency components using the Fourier transform.
Using a Fourier transform to calculate the frequency components, or power spectrum, of a 1Hz square wave reveals the highest power or amplitude in the dominant frequency, with decreasing power in the harmonic frequencies that fill in the square edges.
 
 

Pic Code:
The code can be found here:
Under the hood, the pic is essentially performing the following operations:
(1) Read the data
(2) Low-pass filter
(3) Fill up a signal buffer to pass off to the FFT
(4) Perform the FFT and extract the power in each specified frequency band
(5) Calculate the mean and standard deviation of the power over ~20 minutes
(6) Use the mean and stdev to calculate the normalized changes in power of each band
(7) Change PWM output on LEDs based on deviation of the power from the average
 
 
With this outline in mind, I will now walk through some of the important variables and steps.
SAMPLING_RATE   It is IMPORTANT that this be at least 256Hz. Although the Nyquist frequency dictates that the sampling freq be a minimum of 2x the max freq of interest, at least 4x is much better for getting decent measurements.
FFT_BLOCK_LENGTH   This is how many data points we will use to calculate the FFT. It is IMPORTANT that this is at least 2x a full period of our min freq of interest, better if it's 4x or more. We're using 128/256=1/2=2Hz and our minimum freq of interest is 8Hz, so we're good.
TEMP_BUFF_LENGTH   The length of a temporary buffer we fill up and copy into the signalBuffer. This allows us to perform a fast vector copy 256/32=8 times a second rather than slowly shifting the data on every sample.
fractional (variable type))   Variable type defined on the dspic that is a 16 bit floating point number... really nice for saving lots of space over 32 bit floats when you have large arrays.
x and y data space   The dspic has two different data spaces (x & y) that allow faster math operations if the two variables are put in separate spaces.
brainWaves   The brainWaves struct holds a number of important variables including the frequency ranges of alpha (8-12Hz), beta (14-30Hz) and gamma (30-50Hz) oscillations.
_ISR _T3Interrupt   Our data sampling interrupt set to trigger at 128Hz. It is IMPORTANT that this have a higher priority than other interrupts to avoid irregular sampling.
_ISR _T1Interrupt   This is our LED output interrupt set to trigger approx every 312uSec. 312*32=0.01Sec=100Hz, which is fast enough to fool human eye to thinking it is a constant light source because of persistence of vision (POV). This also gives a 32 step (5 bit) resolution for changing our LEDs from R<->G<->B using pulse-width modulation (PWM), which looks pretty smooth.
misses   Depending on variable settings, it's possible to run out of execution time to complete FFT calculation before another interrupt is triggered. This could cause real problems if the data wasn't done copying from one vector to another. To avoid this, completion of the ProcessData function is tracked and will not reenter until it is finished. A failed attempt to run ProcessData is tracked in this variable.
smoothTemp  
It is CRITICAL to low-pass filter the data before applying the FFT. Any frequency content of the signal that is above the Nyquist frequency is reflected an equivalent amount around the Nyquist. Therefore, if you have a Nyquist of 128Hz, any power at 129Hz will show up as power in the 127Hz bin and so on.

This variable is used to smooth (low-pass filter) the incoming data. This is a hack. An FIR filter (built in to the dspic libraries) should be used instead.

hanningWin  
It is CRITICAL to "taper" or "window" your signal before applying the FFT. The FFT mathematically wraps the signal in a circle, attaching the beginning to the end, so any signal mismatch between these two somewhat arbitrary points in time will lead to massive high frequency noise unless the signal is windowed. Different window functions have different properties, but a Hanning window (right) works for us. Note how below the windowed signal has much less high frequency noise than the same signal without windowing.
SquareMagnitudeCplx   The FFT is actually returning the amplitude and phase information of every frequency. Squaring the output allows us to look only at amplitude (power).
smoothPeriod   After averaging the power over the specified frequency range, we smooth the power in the time-domain to get a more reliable measure.
meanPower   We calculate the mean power over a long period of time (>20 minutes) to get a baseline power that can be used to estimate how our brainwaves are changing.
sdPower   The standard deviation (also calculated from >20 minutes) gives us an estimate of "how large" the changes in power are relative to previous changes.
hue   Calculated from the normalized changes in power ((power - mean)/stdev), hue is the color we want to output on the LED.
hue2rgb   Function to convert the hue into R, G and B values 0-255. These are scaled to 0-31 and define the fraction of time that each color will be on during each PWM cycle.
SetOutputs16bit   Earlier pics had some problems if you updated the out ports too fast. Because the port write function proceeds: port read, delay, port write, it was possible to confuse the pic as to which port were supposed to be on or off if you had multiple port writes in succession within the code. This is my attempt to avoid this problem.
 

 
Also see my other biofeedback wearables / wearable computing accessories.
 
 
produceconsumerobot