PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals

Abstract: This paper presents an inexpensive, high-precision, but at the same time, easy-to-maintain PIEEG board to convert a RaspberryPI to a Brain-computer interface. This shield allows measuring and processing eight real-time EEG (Electroencephalography) signals. We used the most popular programming languages - C, C++ and Python to read the signals, recorded by the device . The process of reading EEG signals was demonstrated as completely and clearly as possible. This device can be easily used for machine learning enthusiasts to create projects for controlling robots and mechanical limbs using the power of thought. We will post use cases on GitHub (https://github.com/Ildaron/EEGwithRaspberryPI) for controlling a robotic machine, unmanned aerial vehicle, and more just using the power of thought.

• The paper introduces a novel, low-cost BCI that repurposes a Raspberry Pi with an 8-channel EEG board for precise real-time signal monitoring.
• It utilizes SPI data transfer up to 16 kSPS with 24-bit resolution and high noise rejection (CMRR of 120) to ensure exceptional signal fidelity.
• The open-source design and integrated software enable real-time artifact detection, paving the way for advanced applications like robotic and drone control.

Overview of PIEEG: A Low-Cost, High-Precision Brain-Computer Interface

The paper "PIEEG: Turn a Raspberry Pi into a Brain-Computer Interface to measure biosignals" by Rakhmatulin and Völkl introduces an innovative solution for transforming a Raspberry Pi into a cost-effective Brain-Computer Interface (BCI). Utilizing a specialized PIEEG board, this approach provides researchers and hobbyists a viable option for measuring and processing electroencephalogram (EEG) signals in real-time.

Technical Contributions and Specifications

The PIEEG board expands the functionality of a Raspberry Pi by accommodating eight EEG channels, enabling comprehensive biosignal monitoring. Key specifications include:

• Data Transfer: Utilizes an SPI protocol with frequencies ranging from 250 SPS to 16 kSPS.
• Resolution and Gain: Offers a 24-bit resolution with multiple gain options (1, 2, 4, 6, 8, 12, 24).
• Noise Rejection: Demonstrates a common-mode rejection ratio (CMRR) of 120, with low internal (0.4 μV) and external (0.8 μV) noise levels.
• Signal Integrity: Offers a signal-to-noise ratio (SNR) of 130 dB, suggesting reliable signal processing.

Such attributes highlight the board's capability to handle EEG signals efficiently, making it a potential tool for various BCI applications, including robotic and drone control via neural inputs.

Software Implementation

The paper provides software solutions to complement the hardware capabilities, focusing on simplicity and efficiency. Data acquisition is managed using C for optimal speed, while Python handles real-time signal processing due to its robust data science ecosystem. This strategic software division ensures a balanced approach between performance and accessibility.

Comparative Analysis

While other devices in the market offer similar functionalities, this paper critiques existing solutions for lacking transparency in component details and open-source methodologies. The proposed PIEEG board stands out by providing detailed engineering files, such as Gerber and BOM files, enhancing reproducibility and credibility. Moreover, its comprehensive integration with Raspberry Pi emphasizes autonomous signal processing, differentiating it from solutions primarily serving as data transmitters.

Experimentation and Results

The authors detail the device's robustness through artifact detection experiments, such as chewing and blinking, demonstrating real-time artifact recognition capabilities. Such tests reinforce its practical applicability in controlling external devices and advancing BCI research without invasive methods.

Future Directions

The authors propose further enhancements including integrated gyroscopes and accelerometers for improved positional control and electromagnetic shielding to boost signal clarity. They also plan to expand software functionalities to include advanced BCI paradigms like P300 and SSVEP, aiming toward more intuitive control mechanisms for prosthetic and robotic applications.

Implications and Conclusion

The PIEEG project presents a significant step toward democratizing BCI technology, providing an accessible platform for experimentation and development in both academic and home-based settings. By lowering the entry cost and complexity of BCI systems, this research has the potential to spur innovation in neurotechnology applications. Future work will likely explore more complex interfacing with external devices and a deeper integration of machine learning techniques for more refined signal interpretation. The commitment to open-source development could facilitate widespread community contributions, driving rapid advancements and practical deployments in various fields.

This research reflects an intersection of hardware innovation, software simplicity, and open-source ethos, contributing substantively to the BCI ecosystem.