Open-source brain-computer interface (OpenBCI) is an affordable bio-sensing system that can sample electrical activity of human body such as brain (EEG), skeletal muscle (EMG) and heart (ECG) activity.


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Estimated cost

Headset: 3D Print it yourself  $300 – Pro-assembled $700

Data acquisition board: 8 channel $100 – 16 channel $950

More info in the OpenBCI Shop


Stable release v.3, MIT license


OpenBCI: Democratizing neurotechnology

OpenBCI stands for open-source brain-computer interface (BCI). The OpenBCI Board is a versatile and affordable analog-to-digital converter that can be used to sample electrical brain activity (EEG), muscle activity (EMG) and heart rate (ECG) amongst others. It is compatible with any type of electrode and is supported by an open-source framework of signal processing applications.

To the view of the developers, the biggest challenges faced in understanding what makes us who we are cannot be solved by a company, an institution, or even an entire field of science. They believe that these discoveries will only be made through an open forum of shared knowledge and concerted effort, by people from a variety of disciplines. Their vision is to realise the potential of the open-source movement to accelerate innovation in brain science.

OpenBCI is a multidisciplinary community of researchers, engineers, artists, scientists, designers and makers amongst others. They share the passion for harnessing the electrical signals of the human brain and body to further understand and expand human mind. As the community continues to grow, so do the possibilities of what they can discover and create together.


Frey, Jérémy. “Comparison of a consumer grade EEG amplifier with medical grade equipment in BCI applications.” International BCI meeting. 2016.

Durka, P. J., et al. “User-centered design of brain-computer interfaces: OpenBCI. pl and BCI Appliance.” Bulletin of the Polish Academy of Sciences: Technical Sciences 60.3 (2012): 427-431.

Chun, Jinsung, Netiwit Kaongoen, and Sungho Jo. “EEG signal analysis for measuring the quality of virtual reality.” Control, Automation and Systems (ICCAS), 2015 15th International Conference on. IEEE, 2015.

Suryotrisongko, Hatma, and Febriliyan Samopa. “Evaluating OpenBCI Spiderclaw V1 Headwear’s Electrodes Placements for Brain-Computer Interface (BCI) Motor Imagery Application.” Procedia Computer Science 72 (2015): 398-405.

Kosch, Thomas, Mariam Hassib, and Albrecht Schmidt. “The Brain Matters: A 3D Real-Time Visualization to Examine Brain Source Activation Leveraging Neurofeedback.” Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2016.

Cassani, Raymundo, Hubert Banville, and Tiago H. Falk. “MuLES: An open source EEG acquisition and streaming server for quick and simple prototyping and recording.” Proceedings of the 20th International Conference on Intelligent User Interfaces Companion. ACM, 2015.


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