Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System

Abstract

brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]- [5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop.An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, with each being associated with a particular intention or mental task. Hence, adaptation is a key component of a BMI because, on the one hand, users must learn to modulate their neural activity to generate distinct brain patterns, while, on the other hand, machine-learning techniques need to discover the individual brain patterns characterizing the mental tasks executed by the user. In essence, a BMI is a two-learner system that must engage in a mutual adaptation process [6], [7].Future neuroprosthetics-robots and exoskeletons controlled via a BMI-will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired limb functions because they are controlled by the same neural signals as their natural counterparts. However, the robust and natural interaction of subjects with prostheses over long periods of time remains a major challenge. To tackle this challenge, we can take inspiration from natural motor control, where

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

“…On the contrary, co-adaptive (a term we use interchangeably to mutual learning) interfaces, in which the capacities of both learning agents—the brain and the machine—are accommodated and coordinated, has been very early proposed as a remedy [ 18 ] and more recently increasingly adopted and modeled as a training strategy [ 19 – 21 ]. Under this view, successful BCI requires that the user and the embedded decoder engage in a mutual learning process, in which users must learn to generate distinct brain patterns for different mental tasks, while machine learning techniques ought to discover, interpret, and allow a model’s adaptation to the potentially changing individual brain patterns associated to these tasks [ 22 ].…”

Brain-Machine Interfaces: A Tale of Two Learners

“…Although the need for mutual learning is widely acknowledged in both invasive and non-invasive BMI [12][13][14][15][16], current research trends are heavily biased towards the machine learning side of BMI training. Here, we take a critical view of the relevant literature and our own previous work, in order to identify key issues for more effective mutual learning schemes in translational BMIs.…”

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

“…Thanks to this feedback, not only the machine would make adjustments, but also the wearer would adaptively learn how to moderate brain signals in order to maintain the proper dialogue with the exoskeleton. This was termed as the "two-learner system" by Millán [134]. Because the exoskeleton aims at assisting patients whose corticospinal connection or descending tracts to the muscles are cut due to lesion, the future brain-controlled exoskeleton should function as the substitution for the spinal cord and the FIGURE 5.…”