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Brain-Computer-Interface controlled robot via RaspberryPi and PiEEG. arXiv:2202.01936

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This paper presents Open-source software and a developed shield board for the Raspberry Pi family of single-board computers that can be used to read EEG signals. We have described the mechanism for reading EEG signals and decomposing them into a Fourier series and provided examples of controlling LEDs and a toy robot by blinking. Finally, we discussed the prospects of the brain-computer interface for the near future and considered various methods for controlling external mechanical objects using real-time EEG signals. Links Source-
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Brain-Computer-Interface controlled robot via RaspberryPi and PiEEG.
Ildar Rakhmatulin* Ph.D. Electronic Researcher
Sebastian Völkl - Brain-Computer-Interface Developer
This paper presents Open-source software and a developed shield board for the Raspberry Pi family of
single-board computers that can be used to read EEG signals. We have described the mechanism for reading
EEG signals and decomposing them into a Fourier series and provided examples of controlling LEDs and
a toy robot by blinking. Finally, we discussed the prospects of the brain-computer interface for the near
future and considered various methods for controlling external mechanical objects using real-time EEG
Source -
Web-Site -
Slack -
License - GNU General Public License v3.0
Keywords: PIEEG, hackerbci, RaspberryPi, EEG, brain-computer interface
BCI Brain-computer interface
EEG Electroencephalogram
SBC Single-board computer
ADC Analog-digital converter
When the term BCI is mentioned, many people immediately associate with controlling objects using the
power of thought. Now, neuroscience in non-invasive EEG measurement is only getting there. Still, each
step brings us closer to that goal and inspires a new generation of scientists and engineers to contribute to
this field of science. We have machine learning, which entered our lives only a few years ago, and more
than enough computing power to look for correlations in EEG signals. The only weakness will be the
availability of the dataset. Therefore, we hope that the existence of a device at a low price will allow us to
take a step towards solving this problem.
Reading EEG signals, despite its apparent simplicity - measuring microvolts from the scalp through
electrodes with a high-precision ADC - is associated with various scientific fields. It involves reading EEG
signals [1, 2022], processing EEG signals [2, 2021], selecting features, and finally using the signals for
various purposes. Moreover, blinks or chews are unwanted artifacts that introduce harmful distortions into
EEG signals, and many works are devoted to combating these artifacts [3, 2022; 4, 2022]. At the same time,
however, these artifacts are still commonly used in applied tasks, such as blink control of external objects.
Lin et al. [5, 2010] successfully controlled an electric wheelchair by blinking through a brain-computer
interface. Huang et al. [6, 2019] developed an application to control the integrated system of a robotic
wheelchair by blinking and BCI.
Our board is designed to familiarize everyone with the world of EEG, including those not directly related
to the field of neurology. So, our mission is to lower the threshold of technical knowledge to get started
with BCI. Our goal was not to compete with the previously described papers and show that our control
method has the best performance. Considering all the shortcomings of this method (competition with eye-
tracking, low efficiency, limited freedom of action, and complexity in administration), we believe this
method is an excellent example to demonstrate the capabilities of BCIs.
1. Technical details
The technical points of the developed device PIEEG were presented in the article [7, 2022]. Therefore, in
this paper, we only limit ourselves to the main technical details of the device in table 1.
Table 1. PIEEG characteristic
Fig. 1 schematically shows the use of the developed device for robotic objects control.
a b
Fig. 1.a - Schematic representation of the use of PIEEG, b -8 electrodes connected by according to the
International 10-20 system
We used dry electrodes from Florida research instruments (
eeg-electrodes). An overview of dry electrodes, their uses, and disadvantages we have described in the
paper [10, 2021].
2. Software
An overview of software for processing EEG signals was given earlier in this paper [8, 2021]. The main
motive for developing our own software rather than integrating it into existing platforms is the expediency
of constantly changing and improving the software, for example, for mind control of the robot. We have
made the connection of RaspberryPi and ADC in C language and created a static library and implemented
the subsequent signal processing in Python. The static library sets the signaling rate and registers for setting
up the ADC. For us, it is essential that when a Python file is corrected, the library does not need to be
recompiled. Figure 2 shows the process of measuring EEG signals and blink artifacts.
Fig.2. Blinking with EEG signal on PIEEG (real-time)
As shown in the figure, the response in the EEG signals to the blink is available on all channels, so in the
research, it is sufficient for us to use only one electrode; we chose the electrode in position - Fz.
3.1 Device setup
The position of the electrode can be changed and adjusted for both chewing detection and blinking. For the
measurement process, the PIEEG should be connected to the Raspberry PI3 or RaspberryPI4. The power
supply for the RaspberryPi must be connected to a battery - 5V (do not use the power network).
The method we chose for feature selection is the Fast Fourier Transform, where the signal is decomposed
into frequencies [9, 2021]. Repetitive actions such as blinking or chewing. It is worth noting that blinking
at a specific frequency is quite tricky, which is done intuitively. The signal amplitude is different for each
person (the response of EEG signals to blinking), so the settings for the frequency range of the response
and the amplitude of the signal are found by each user through experimental methods. In Figs. 3 and 4, we
show the results of controlling two LEDs by blinking. We found amplitude peaks in the frequency ranges
of 5 Hz and 3 Hz for the EEG signal decomposed by Fast Fourier Transform.
Fig.3. LED control in the range of 3 - 7 Hz. a (upper graph) - EEG signal through pass filter 1-30 Hz. an
(upper c) EEG signal (the part circled in purple from the graph above) received through the Fast Fourier
Transform. The red line is the setting for turning on the LED, green lines are the frequency range in which
we monitor the signal amplitude. b LED circled in blue, output - 31 for GPIO.setmode (GPIO.BOARD)
Fig.4. LED control in the range of 1 - 3 Hz. a (upper graph) - EEG signal through pass filter 1-30 Hz. a
(lower upper graph) EEG signal (the part circled in purple from the graph above) received through the
Fast Fourier Transform. Redline is the setting for turning on the LED, green lines are the frequency range
in which we monitor the signal amplitude. b LED circled in blue, output - 35 for GPIO.setmode
The result is positive but does not look ideal. The accuracy can be improved by setting other ranges for the
bandpass filter, changing the location of the electrode, changing ADC registers, et cetera. In our case, the
instrument worked correctly in 8 out of 10 cases for both frequency ranges. This error can be caused by the
hardware characteristics of the instrument, the software, and human factors. For better illustration, we did
not connect the signals to the LED but the remote control of the radio-controlled toy and were able to
successfully control the toy’s movement (Fig. 5).
Fig.5. Connecting toys. Discrete signals are connected to the control buttons of the radio remote control.
Circled in red and green squares.
The toy is presented only as an example, any type of device can be connected.
We suspect that controlling robots by thought would ideally have more to do with motor imagery. However,
that has not been done with non-invasive electrodes yet. All the studies we have analyzed generally work
in two coordinates - x and y, and the z coordinate. Therefore, the ease of implementation of this method
will hopefully inspire engineers to move forward in this direction. We still believe that non-invasive
electrodes will soon achieve better results, thanks to the power of machine learning, allowing more natural
control of robots.
We did not want to show that blinking is the best way to control a robot, but we wanted to use cheap devices
to demonstrate how to control external objects using EEG signals. Paradigms such as P300 and ERPs that
allow printing on the keyboard can also be used in similar scenarios.
Now the software works in real time with a delay of 1 second, if necessary, the reaction speed can be
increased. One of the advantages of working with a shield is that the data from the ADC is transferred
directly to the processor using the SPI protocol. We do not need to use adapters, transmitters, such as
Bluetooth, Wi-Fi, Serial. Ideally, when implementing software in a lower programming language, the
response speed allows us to receive data at the transfer rate set in the ADS1299 - 250 SPS to 16 kSPS.
The shield can be installed on the OrangePi and BananaPi series SBCs. It is worth noting that the connection
to the boards is made through the power supply, SPI channels and several discrete outputs, so the devices
can be connected to any SBCs through wires if the GPIO channels do not match. All the software is in the
public domain and is under an open license, all rights and comments are welcome. We will continue to
work on the software and the shield in the future.
1. Yu, H,. Ba, S,. Guo, Y., Guo, L., Xu, G. (2022). Effects of Motor Imagery Tasks on Brain Functional
Networks Based on EEG Mu/Beta Rhythm.Brain Sci.,12, 194.
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Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in
Schizophrenia. Electronics, 10, 3037.
3. McDermott, E.J.; Raggam, P. (2022). Artifacts in EEG-Based BCI Therapies: Friend or Foe? Sensor ,22,
4. Maddirala, K.; Veluvolu, C. (2022). SSA with CWT and k-Means for Eye-Blink Artifact Removal from
Single-Channel EEG Signals. Sensors,22, 931.
5. Lin, J., Chen, K., Yang, W. (2010). EEG and eye-blinking signals through a Brain-Computer Interface
based control for electric wheelchairs with wireless scheme. 4th International Conference on New Trends
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6. Huang, Q., Zhang, Z., Yu, T., He, S., & Li, Y. (2019). An EEG-/EOG-Based Hybrid Brain-Computer
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7. Rakhmatulin, I., Volkl, S. (2022). PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to
measure biosignals, arXiv:2201.02228
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Preprints, 2021030555. https://doi:10.20944/preprints202103.0555.v1
ResearchGate has not been able to resolve any citations for this publication.
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PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals
  • I Rakhmatulin
  • S Volkl
Rakhmatulin, I., Volkl, S. (2022). PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals, arXiv:2201.02228