1. Brain-Computer-Interface for Stress Detection Hassan Farooq, Ilona Wong Supervisor: Steve Mann Administrator: Cristiana Amza Section 8 Collecting Brainwave Data Processing Data Conclusions and Future Work Our SystemBackground TRIFOLD AREA – THIS GUIDE WILL BE REMOVED BEFORE PRINTING – TRIFOLD AREA – THIS GUIDE WILL BE REMOVED BEFORE PRINTING – TRIFOLD AREA – THIS GUIDE WILL BE REMOVED BEFORE PRINTING – TRIFOLD AREA – THIS GUIDE WILL BE REMOVED BEFORE PRINTING – TRIFOLD Electrical data from the brain, collected through electroencephalography (EEG) techniques, represents an amalgamation of the underlying processes of the brain. EEG has many medical uses such as differentiating epileptic seizures from other types of brain disorders like migraines, and even being used to diagnose and find prognosis for comatose patients. However, the EEG machines that hospitals use cost thousands of dollars, can only be used in the hospital, and are complex so they require trained operators to use. Our objectives were to create 1.An affordable and wearable device that is usable at all times 2.A device that can interpret EEG signals and 3.A device that can help the detection and management of stress. Our system has three main components: 1.Collection of brainwave data from the user’s forehead 2.Processing the data using Fourier and Chirplet transforms 3.Classifying the mental state as stressed or unstressed We collect brainwave data using the NeuroSky MindWave. In the beginning, we tried to interface the wireless dongle with the FPGA, but due to its sensitivity to power fluctuations, we connected the headset directly to the PMOD connector on the FPGA, seen in the picture on the left. Using NeuroSky’s protocol, we parsed the bits on the FPGA to obtain raw data. Current Technology In recent times, the field of experimental neuroscience has advanced to a point where brainwave data can be collected with relative ease and accuracy. Advantages: These new devices are available to the average consumer at a fraction of the cost of their predecessors. Disadvantages: Consumer products still need to be used at a computer. Professor Steve Mann’s Mindmesh is wearable, however it is custom made to fit his head and also requires the user to shave their head. This motivated us to make an affordable and wearable EEG device, i.e. it can work without the need to be tethered to a computer. Stress Prediction Chirplet Transform: We use a State Vector Machine to classify the data. The use of the FPGA with its many I/Os allows us to easily add more features to this system. As the processing center of a BCI, this design could be made completely portable and allow sufferers of stress or other mental disorders to handle their condition better. By providing constant feedback of the onset of stress or its causes, this system could assist doctors in diagnosing patients. The raw data being provided is very noisy and needs to be filtered before it can be used for classification. We use two methods: Fourier Transform and Chirplet Transform Fourier Transform: This was implemented in hardware using Xilinx’s System Generator. The block diagram below is a snapshot of our implementation. Results From left to right: Steve Mann’s Mindmesh, the Emotiv Epoc, the NeuroSky MindWave From left to right: Connecting the MindWave directly to the FPGA, An attempt to connect the dongle to the FPGA Chirplet decomposition of a bat echo- location signal. Counter-clockwise from left: a) The time domain representation of the sound signal. b) The spectrogram of the signal. c) The chirplet decomposition of the signal consisting of 5 chirplets. Images courtesy of Talakoub et al. Counter-clockwise from top-left: Plot of raw voltage data, magnitude of FFT, Chirplet Transform, Phase of FFT, stress prediction over time. We achieved our goal of parsing and processing the raw data from the MindWave at 512 Hz and implemented near-real time machine learning prediction. The only aspect of the design that is not real time but close (~5 seconds for 1 second of data) is the chirplet transform. The prediction algorithm achieved 60% accuracy on our test set.