To restore movement in patients with severe neuromotor disabilities, approaches based on Long Short-Term Memory decoders could provide better algorithms for neuroprostheses that employ Brain-Machine Interfaces, stated study published in the journal Neural Computation.
This investigation was carried out by researchers of Duke University (USA) and has involved Núria Armengol, an alumna of the bachelor's degree in Biomedical Engineering at UPF who initiated this research topic for her end-of-degree project under the supervision of Ruben Moreno Bote, a researcher at the Center for Brain and Cognition (CBC) of the Department of Information and Communication Technologies (DTIC) at UPF, which she developed at Duke University (Durham, USA). Currently, Armengol is pursuing a master's degree at the Swiss Federal Institute of Technology in Zurich (ETH, Switzerland).
Although over the years many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications, recent advances in deep learning algorithms have improved the design of brain activity decoders involving recurrent artificial neural networks capable of decoding the activity of all neurons in real time.
"Our LSTM algorithm significantly outperformed the Kalman filter (an analytical method that enables estimating unobservable state variables from observable variables) while the monkeys were performing different tasks on a treadmill (raising an arm, raising both arms or walking)", Armengol adds.
Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics during tasks. LSTM modelled several key physiological attributes of the cortical circuits involved in motor tasks.