Biomimetic Brain Machine Interfaces for the Control of Movement

Abstract

Quite recently, it has become possible to use signals recorded simultaneously from large numbers of cortical neurons for real-time control. Such brain machine interfaces (BMIs) have allowed animal subjects and human patients to control the position of a computer cursor or robotic limb undertheguidanceofvisualfeedback.Althoughimpressive,suchapproachesessentiallyignorethedynamicsofthemusculoskeletalsystem,and they lack potentially critical somatosensory feedback. In this mini-symposium, we will initiate a discussion of systems that more nearly mimic the control of natural limb movement. The work that we will describe is based on fundamental observations of sensorimotor physiology that have inspired novel BMI approaches. We will focus on what we consider to be three of the most important new directions for BMI development related to the control of movement. (1) We will present alternative methods for building decoders, including structured, nonlinear models, the explicit incorporation of limb state information, and novel approaches to the development of decoders for paralyzed subjects unable to generate an output signal. (2) We will describe the real-time prediction of dynamical signals, including joint torque, force, and EMG, and the real-time control of physical plants with dynamics like that of the real limb. (3) We will discuss critical factors that must be considered to incorporate somatosensory feedback to the BMI user, including its potential benefits, the differing representations of sensation and perception across cortical areas, and the changes in the cortical representation of tactile events after spinal injury.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

“…The field of BMIs is expanding rapidly, and a variety of methods for decoding cortical signals have been proposed. One method of obtaining a control signal for BMIs is to decode EMG signals from cortical recordings, and several groups are actively exploring this decoding paradigm (Fagg et al 2007;Nazarpour et al 2012;Pohlmeyer et al 2007;Santucci et al 2005). One advantage EMG decoding has over kinematic decoding is that EMG signals are related to movement dynamics.…”

“…Linear decoders are common, due to their simplicity and ease of use, as well as their effectiveness in extracting behavioral variables from neuronal activity (Lebedev et al 2008;Wessberg et al 2000). Linear algorithms applied to decoding EMG signals from cortical activity include the Wiener filter (Carmena et al 2003;Fitzsimmons et al 2009;Santucci et al 2005) and multiple input-single output systems (Pohlmeyer et al 2007), both of which make predictions based on a combination of weighted neural activity (Pohlmeyer et al 2007). Other approaches incorporate nonlinear elements, such as the Wiener cascade, which is a dynamic linear system that includes a fixed nonlinear element (Pohlmeyer et al 2009).…”

Brain-Machine-Brain Interface

“…Since then interest in prosthetic somatosensation has dramatically increased (Fagg et al, 2007). London and colleagues demonstrated that rhesus monkeys can be cued by patterns of ICMS applied to the area 3a subdivision of primary somatosensory cortex, an area that is generally believed to be involved in the processing of proprioception and kinesthesia.…”

Decoding of Kinetic and Kinematic Information from Electrocorticograms in Sensorimotor Cortex: A Review

“…These BMI systems translate brain signals into commands for controlling devices such as cursors [1], spelling devices [2], and neural prosthetics [3][4][5][6][7][8][9]. This new communication has not only the potential to help to disabled persons but also provide insight into the motor system of the brain [10][11][12][13][14].Several sensors have been developed to measure brain signals. These are mainly categorized into two types, invasive sensor i.e.…”