Lastly, mu waves occur in the 8-13 Hz frequency range while motor neurons are at rest. Gamma waves in the 30-100 Hz range occur during sensory processing of sound and sight. They become present while the user is concentrating. Beta waves reside in the 13-30 Hz frequency band and are characteristic of the user being alert or active. Another way to boost alpha waves is to close the eyes. Alpha waves have frequency range 8-14 Hz and take place while relaxing or reflecting.
Theta waves occur within the 4-8 Hz frequency band during meditation, idling, or drowsiness. Delta waves are characteristic of deep sleep and are high amplitude waves in the frequency range $0 \leq f \leq 4$ Hz.
The EEG signal itself has several components separated by frequency.
If we let $N \rightarrow \infty$, then the filter becomes an infinite impulse response (IIR) filter. Note that only $N$ coefficients are used for this filter (hence, "finite" impulse response). The filter equation in terms of the output sequence $y$ and the input sequence $x$ is: This $h$ function "characterizes" the LTI system. In signal processing, the output $y$ of a linear time-invariant (LTI) system is obtained through convolution of the input signal $x$ with its impulse response $h$. In order to filter the brain wave data in MATLAB, we use a finite impulse response (FIR) filter which operates on the last $N+1$ samples received from the ADC. We take advantage of this speed-up to perform DFTs in real-time on the input signals. Source: Īn algorithm, the Fast Fourier Transform (FFT) by Cooley and Tukey, exists to perform DFT in $O(n\log n)$ computational complexity as opposed to $O(n^2)$. During training, the SVM is given a set of instance-label pairs of the form $\)$. A brief explanation of the mathematics behind SVMs follows. The supervised learning method takes a set of training data and constructs a model that is able to label unknown test data. The machine learning algorithm we used was a support vector machine (SVM), which is a classifier that operates in a higher dimensional space and attempts to label the given vectors using a dividing hyperplane. Moreover, our project has diverse applications in the areas of neurofeedback (aiding meditation and treatment of ADHD disorder), along with brain-computer interfaces (allowing the disabled to control wheelchairs and spell words on a computer screen using their thoughts). Our goal was to build a low-cost alternative that would allow users to take their health in their own hands by diagnosing and attempting to treat their own sleep disorders. Moreover, the patient often is denied access to their own data since a licensed sleep specialist interprets it for them. This process is costly and requires an overnight stay at a hospital or sleep lab. The patient's sleeping patterns are recorded overnight, and apneas (periods of sleep without breathing) can be identified within the collected data. In order to diagnose sleep apnea, a clinical sleep study is performed where the patient is attached to EEG electrodes, along with SpO 2, EMG, and respiration sensors. Our project idea was inspired by Charles's severe obstructive sleep apnea (OSA) disorder. High-Level Design Rational and Inspiration for Project Idea
#Waves nx trial remove software#
We also wrote software to record our sleep and store the EEG signal inside a data file.įigure: Recording a User's Brain Waves Using EEG
#Waves nx trial remove Pc#
From there, we were able to control our own OpenGL implementation of the classic PC game Pong using our mind's brain waves. The PC runs software written in MATLAB and C to perform FFT and run machine learning algorithms (SVM) on the resultant signal. The opto-isolated UART sends the ADC digital values over USB to a PC connected to the microcontroller. Passive silver-plated electrodes soaked in a saline solution are placed on the user's head and connected to the amplifier board. Moreover, we used the built-in ADC functionality of the microcontroller to digitize the signal. In order to accomplish this, we constructed a two-stage amplification and filtering circuit. We decided that the least invasive way of measuring brain waves would be using electroencephalography (EEG) to record microvolt-range potential differences across locations on the user's scalp. Our goal was to build a brain-computer interface using an AVR microcontroller.