Maryam Shanechi, the Sawchuk Chair in Electrical and Computer Engineering and founding director of the USC Center for Neurotechnology, and her team have developed a new AI algorithm that can separate brain patterns related to a particular behaviour. This work, which can improve brain-computer interfaces and discover new brain patterns, has been published in the journal Nature Neuroscience. 

Unlocking the Brain’s Code for Restoring Paralyzed Movement 

While you are reading this article, your brain is involved in multiple behaviours. Perhaps you are moving your arm to grab a cup of coffee while reading the article out loud for your colleague and feeling a bit hungry. All these behaviours, such as arm movements, speech, and different internal states, such as hunger, are simultaneously encoded in your brain. This simultaneous encoding gives rise to complex and mixed-up patterns in the brain’s electrical activity. Thus, a significant challenge is dissociating brain patterns that encode a particular behaviour, such as arm movement, from all other brain patterns. 

For example, this dissociation is vital for developing brain-computer interfaces that aim to restore movement in paralyzed patients. When thinking about making a movement, these patients cannot communicate their thoughts to their muscles. To restore function in these patients, brain-computer interfaces decode the planned movement directly from their brain activity and translate that to moving an external device, such as a robotic arm or computer cursor. 

DPAD Unlocks Hidden Brain Patterns for Better BCI 

Shanechi and her former Ph.D. student, Omid Sani, who is now a research associate in her lab, developed a new AI algorithm that addresses this challenge. The algorithm is named DPAD for “Dissociative Prioritized Analysis of Dynamics.” 

“Our AI algorithm, named DPAD, dissociates those brain patterns that encode a particular behaviour of interest, such as arm movement, from all the other brain patterns that are happening at the same time,” Shanechi said. “This allows us to decode movements from brain activity more accurately than prior methods, which can enhance brain-computer interfaces. Further, our method can also discover new patterns in the brain that may otherwise be missed.” 

“A key element in the AI algorithm is to first look for brain patterns related to the behaviour of interest and learn these patterns with priority during training of a deep neural network,” Sani added. “After doing so, the algorithm can later learn all remaining patterns so they do not mask or confound the behaviour-related patterns. Moreover, using neural networks gives ample flexibility in terms of the types of brain patterns the algorithm can describe.” 

Tracking Mental Health Symptoms with AI Precision 

In addition to movement, this algorithm has the flexibility to potentially be used in the future to decode mental states such as pain or depressed mood. Doing so may help better treat mental health conditions by tracking a patient’s symptom states as feedback to precisely tailor their therapies to their needs. 

“We are very excited to develop and demonstrate extensions of our method that can track symptom states in mental health conditions,” Shanechi said. “Doing so could lead to brain-computer interfaces not only for movement disorders and paralysis but also for mental health conditions.” 

Source: USC Viterbi