Ildar Rakhmatulin Is Back with More Low-Cost Brain-Computer Interface Tech: Meet the ardEEG

Neuroscience researcher and maker Ildar Rakhmatulin has launched a shield for the Arduino UNO R4 WiFi, which adds support for electroencephalograph (EEG), electromyograph (EMG), and electrocardiograph (ECG) sensors — in the hopes of making it easier to experiment with brain-computer interfaces (BCIs).

"Many years ago, like probably many others, I began to learn about electronics through Arduino," Rakhmatulin writes of the inspiration behind the project. "Now I have started testing the ardEEG shield for Arduino UNO R4 WiFi to read EEG, EMG, and ECG signals. In other words, the ardEEG shield can convert Arduino UNO R4 WiFi into a brain-computer interface."

This isn't Rakhmatulin's first attempt to make brain-computer interface experimentation more accessible. Back in late 2021 he unveiled the PIEEG, a similar interface for adding EEG, EMG, and ECG sensing to projects — but serving as an accessory for the Raspberry Pi single-board computer range, rather than as a shield for lower-cost Arduino microcontrollers.

A crowdfunding campaign for the PIEEG board closed in April last year, and as of April this year Rakhmatulin had shipped the last units — eight-channel models, compared to the four-channel versions which had shipped earlier — to backers.

Like the later PIEEG models, the ardEEG provides eight input channels compatible with electroencephalograph (EEG), electromyograph (EMG), and electrocardiograph (ECG) sensors — measuring brain activity, muscle activity, and heart activity respectively. Captured data is transferred to the host microcontroller at up to 16,000 samples per second at a 24-bit resolution, with open source Python packages provided for data processing.

Pricing for the ardEEG has yet to be finalized, but the board is expected to sell for less than the $350 of the eight-channel PIEEG variant — plus, of course, the cost of an Arduino UNO R4 WiFi board and suitable electrode pads on top. More information is available on the PIEEG website, with documentation and software available on the project's GitHub repository.