Many human movements, such as walking or reaching, follow predictable patterns, too. Limb position, speed and several other movement features tend to play out in an orderly way. With this regularity in mind, Eva Dyer, a neuroscientist at the Georgia Institute of Technology, decided to try a cryptography-inspired strategy for neural decoding.
Existing brain-computer interfaces typically use so-called ‘supervised decoders.’ These algorithms rely on detailed moment-by-moment movement information such as limb position and speed, which is collected simultaneously with recorded neural activity. Gathering these data can be a time-consuming, laborious process. This information is then used to train the decoder to translate neural patterns into their corresponding movements. (In cryptography terms, this would be like comparing a number of already decrypted messages to their encrypted versions to reverse-engineer the key.)
By contrast, Dyer’s team sought to predict movements using only the encrypted messages (the neural activity), and a general understanding of the patterns that pop up in certain movements.
Her team trained three macaque monkeys to either reach their arm or bend their wrist to guide a cursor to a number of targets arranged about a central point. At the same time, the researchers used implanted electrode arrays to record the activity of about 100 neurons in each monkey’s motor cortex, a key brain region that controls movement.
To find their decoding algorithm, the researchers performed an analysis on the neural activity to extract and pare down its core mathematical structure. Then they tested a slew of computational models to find the one that most closely aligned the neural patterns to the movement patterns.
Because Dyer’s decoder only required general statistics about movements, which tend to be similar across animals or across people, the researchers were also able to use movement patterns from one monkey to decipher reaches from the neural data of another monkey—something that is not feasible with traditional supervised decoders.