Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

Abstract

Objective It is quite remarkable that Brain Machine Interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach We have developed a system that uses two independent Weiner filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The classifier combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter

“…If the combination of various online linear decoders and intention estimation or adaptation techniques does indeed prove insufficient, there are multiple design directions which may be fruitful. One could develop a more accurate classification scheme to account for and suppress unwanted finger movements, similar to work in reach tasks (Aggarwal et al, 2013 ; Sachs et al, 2016 ) and cursor control (Kao et al, 2017 ). Another study effectively reduced the numbers of DOF of the hand using dimensionality reduction (Rouse, 2016 ).…”

Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law

“…Recently, a non-linear, two state decoder that can switch between a postural decoder and a movement decoder (Sachs et al 2015) increased the ability of the user to stop on smaller targets. More generic non-linear decoders could automatically extract information about stopping or about movement scale in general, if it exists (Sussillo et al 2012; Li et al 2009).…”

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces

“…When an idle or hold state was detected, the decoder output is ignored and movement was set to zero. Recently, Sachs et al (2016) detected posture vs. movement states using linear discriminant analysis and used Wiener filters with different coefficients during each.…”