Ildar RakhmatulinPiEEG offers a seamless and affordable solution for capturing brain signals and unlocking their potential for various applications. Join us in revolutionizing the field of neuroscientific exploration and practical implementation with PiEEG.
Presentation
Robot toy control via blinking
In the lower graph demonstrated only 1/8 of the upper graph from the right side after The Fast Fourier Transform (FFT)
1. Demonstration - the EEG signal (upper graph) converted to a Fourier series (lower graph). We can see in the lower graph for various frequency ranges, dependence amplitudes from the frequency of blinking (5 Hz and 3 Hz)
2. We set the threshold to the amplitude and connected two diodes. If the amplitude (lower graph) at a frequency of 1-3 Hz becomes more than the threshold, then the upper LED will light up.
If the amplitude (lower graph) at a frequency of 3-5 Hz becomes more than the threshold, then the lower LED will light up.
3. Everything is the same, but instead of diodes, we connected output discrete signals to the toy radio control panel and controlled the mouse (ahead, back).
Instead of a toy mouse, you can connect whatever you want.
Everybody can do this device for free himself, all needed sources available on my GitHub
https://github.com/HackerBCI/EEGwithRaspberryPI
or use my instructions from here
My papers about PiEEG (all papers, all research, and work were made in my free time outside of academic activity, without any financial or other types of support)
1. Rakhmatulin, I., Parfenov, A., Traylor, Z. et al. (2021). Low-cost brain computer interface for everyday use. ExpBrain Res 239, 3573–3583. https://doi.org/10.1007/s00221-021-06231-49
2. Rakhmatulin, I. (2021). ironbci - Open-source Brain-computer interface with the embedded board to monitor the physiological subject's condition and environmental parameters arXiv:2111.03656 https://arxiv.org/abs/2111.03656
3. Rakhmatulin, I . (2021). The electronic board to replace the reference voltage on the earlobe for EEG measurement. March 2021Measurement 173 DOI: 10.1016/j.measurement.2020.108673 https://www.sciencedirect.com/science/article/abs/pii/S0263224120311854
4. Rakhmatulin, I. et al. (2021). Raspberry PI Shield - for measure EEG (PIEEG). Conference: 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). DOI: 10.1109/ICEECCOT52851.2021.9707969 https://ieeexplore.ieee.org/document/9707969
5. I Rakhmatulin, S Volkl. (2022). Brain-Computer-Interface controlled robot via RaspberryPi and PiEEG, arXiv preprint arXiv:2202.01936 https://arxiv.org/abs/2202.01936
6. Rakhmatulin, I. (2020). Review of EEG Feature Selection by Neural Networks. Int. J. Sci. Bus. 2020, 4, 101–112. http://dx.doi.org/10.2139/ssrn.3675950
7. Rakhmatulin, I. (2020). Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism. https://doi.org/10.1101/2021.06.02.21258251
8. I Rakhmatulin, S Volkl. (2022). PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals. arXiv:2201.02228 https://arxiv.org/abs/2201.02228
Media about PiEEG
https://hackaday.com/2023/04/03/pieeg-offers-affordable-brain-computer-interface/
https://www.raspberrypi.com/news/raspberry-pi-to-brain-interface/
https://spectrum.ieee.org/neurotechnology-diy
https://www.tomshardware.com/news/control-a-raspberry-pi-with-your-mind-and-pieeg
https://www.electronics-lab.com/pieeg-a-low-cost-raspberry-pi-based-brain-computer-interface/
and other
And in the summation
Using a Raspberry Pi to measure EEG signals offers several advantages that make it a preferable choice for this purpose:
- Cost-effectiveness: Raspberry Pi boards are affordable and readily available, making them a cost-effective solution for EEG measurements. This affordability allows for wider accessibility to EEG technology and facilitates research and experimentation in the field.
- Flexibility and versatility: Raspberry Pi is a versatile platform that supports various operating systems and programming languages. This flexibility enables researchers and developers to customize and adapt their EEG measurement setups according to their specific requirements.
- User-friendly interface: Raspberry Pi provides a user-friendly interface, making it accessible even to individuals without extensive technical expertise. Its intuitive software and easy connectivity options simplify the installation and operation of the EEG measurement system.
- Open-source community: Raspberry Pi has a large and active open-source community that continuously develops and shares resources, software libraries, and tutorials. This collaborative environment fosters innovation and facilitates the development of new applications and techniques in the field of EEG research.
- Integration capabilities: Raspberry Pi boards come with a range of input/output (I/O) pins that can be utilized to connect and interface with various sensors and devices. This integration capability allows for seamless connectivity with EEG electrodes and other auxiliary equipment, enhancing the overall functionality of the measurement system.
- Compact and portable: Raspberry Pi boards are compact in size, allowing for portability and convenience. This feature is particularly beneficial for field research, remote monitoring, or applications that require mobility.
Plans
I am working on software that will allow easy control of stress levels, and concentration for the meditation process, and for sleep control.