X, Alphabet’s experimental research and development lab, today detailed Project Amber, which aims to make brain waves as easy to measure and interpret as blood glucose levels. The goal is to develop an objective measurement of depression that could be used to support diagnoses, treatment, and therapies.
The Amber team sought to marry machine learning techniques with electroencephalography to measure electrical activity in the brain.
Inspiration arose from the observation that game-like tasks can be used to gauge processing within the brain’s reward system. Brain response following a win in a game is subdued in people who are depressed, compared with those who are not.
X isn’t the first to apply machine learning algorithms to EEG readings.
EEGs have been widely used to study swallowing, classify mental states, and diagnose neuropsychiatric disorders such as neurogenic pain and epilepsy, as well as to classify emotions. It took three years for the Amber team to design a low-cost, portable, research-grade system designed to make it easier to collect EEG data.
It features an accompanying bioamp that can can support up to 32 channels, and it can be used to collect resting state EEG and event-related potentials with software that time-locks a task to the EEG measurement. Beyond the headset, the Amber team explored how new approaches in machine learning could be used to reduce unwanted noise in EEG recordings.
Collaborating with DeepMind, Alphabet’s AI reserch lab, they adapted methods from unsupervised representation learning to address these challenges. First, they demonstrated that representation learning approaches like autoencoders could be tapped to de-noise EEG signals without a human in the loop.
Unlike previous studies, the Amber team claims they were able to do this for an individual participant rather than a group.
“The methods were capable of recovering usable signal representations from single EEG trials,” X head Obi Felten explained in a blog post.
The Amber team was ultimately unsuccessful in finding a single biomarker for depression and anxiety.
The Amber team is donating unused EEG headsets to Sapien Labs, which runs the Human Brain Diversity Project supporting EEG research in low-income countries and with underrepresented groups, and making a pledge not to assert its patents on Amber’s hardware.
“We hope that open-sourcing our EEG system and publishing our machine learning techniques will be of value not just to EEG experts, but also to the wider mental health research community who were perhaps put off by the complexity and cost of working with EEG before,” Felten wrote.
“There are many pitfalls on the path to making tech-enabled mental health measurement work in the real world, and more research needs to be done Addressing today’s challenges will require new partnerships between scientists, clinicians, technologists, policymakers, and individuals with lived experience.”