Unsupervised decoder initialization for brain-machine interfaces using neural state space dynamics

Abstract

One of the key elements in the design of neuromotor Brain-Machine Interfaces (BMIs) is the neural decoder design. In a biomimetic approach, the decoder is typically trained from concurrent recordings of neural and kinematic or motor imagery data. The non-availability of the latter data imposes a practical problem for patients with lost motor functions. An alternative approach is a biofeedback approach in which subjects are encouraged to 'learn' an arbitrary mapping between neural activity and the external end effector. In this work, we propose an unsupervised decoder initialization scheme to be used in the biofeedback approach that alleviates the need for synchronized kinematic or motor imagery data for decoder training. The approach is totally unsupervised in that the recorded neural activity is directly used as training data for a decoder designed to provide 'desirable' features in the decoded control signal. The decoder is trained from 'spontaneous' neural data when the BMI subject is not engaged in any behavioral task, and we demonstrate its ability to generalize to neural data collected when the subject is in a different behavioral state.

Ultra-long term stability of single units using chronically implanted multielectrode arrays

“…Decoders using the neural population activity of either group, binned at 50 milliseconds, were initialized in an unsupervised manner using spontaneous data [8]. Over the course of the study, the macaque learned this mapping such that she could coordinate movement along both dimensions in order to perform successful trials at an increasing rate.…”

Neuroplasticity subserving the operation of brain–machine interfaces

“…With the exception of human studies [36] [10, 11, 37] and one recent animal study [38-40], demonstrations of BMIs have been primarily carried out in able-bodied subjects. During manual control (MC) in instructed delay tasks, a monkey is trained to make real arm movements from one starting position to a target – or continuously pursue a randomly appearing target – after which the arm returns to the starting position before a new trial starts [41, 42].…”

“…The neurofeedback concept has been recently extended to condition spatiotemporal patterns of activity from multiple neurons simultaneously recorded in the hand and forearm areas of the primary motor cortex of two non-human primates with chronic transradial and transhumeral amputation – the first BMI study with an animal having a chronic form of disability [38, 40]. Using recordings from ‘stable’ populations of M1 neurons [39], units were divided into a number of non-overlapping clusters of functionally connected neurons – as measured by spike train correlations during periods of spontaneous activity [72].…”

“…Using recordings from ‘stable’ populations of M1 neurons [39], units were divided into a number of non-overlapping clusters of functionally connected neurons – as measured by spike train correlations during periods of spontaneous activity [72]. An unsupervised, non-biomimetic decoder was built from offline analysis of spontaneous neural activity to enable the monkeys to control multiple DOFs [40]. Monkeys learned to control Cartesian and hand grasp velocities in a self-paced reach-to-grasp task.…”

“…In contrast, perturbations to the projections of the high -dimensional representation—labeled outside-manifold perturbations and hence “less intuitive”—were harder to learn within the same session. Notably, the “more intuitive mapping” contended to exist in the within manifold perturbation included both biomimetic and non-biomimetic decoder mappings that could be learned by the monkeys, as long as the non-biomimetic decoder was constrained by the span of the neural co-modulation patterns, which has been suggested by an earlier study [40]. Nonetheless, the study did not test across-session learning – a critical element in the comprehensive assessment of the true extent of skill learning as opposed to mere sensorimotor adaptation that is known to occur within single sessions.…”