Toward Synergy-Based Brain-Machine Interfaces

Abstract

This paper demonstrates a synergy-based brain-machine interface that uses low-dimensional command signals to control a high dimensional virtual hand. First, temporal postural synergies were extracted from the angular velocities of finger joints of five healthy subjects when they performed hand movements that were similar to activities of daily living. Two synergies inspired from the extracted synergies, namely, two-finger pinch and whole-hand grasp, were used in real-time brain control, where a virtual hand with 10 degrees of freedom was controlled to grasp or pinch virtual objects. These two synergies were controlled by electrocorticographic (ECoG) signals recorded from two electrodes of an electrode array that spanned motor and speech areas of an individual with intractable epilepsy, thus demonstrating closed loop control of a synergy-based brain-machine interface.

Application of Linear and Nonlinear Dimensionality Reduction Methods

“…Also shown in the figure are the hand postures of the reconstructed movement across time. Adapted from Vinjamuri et al (2011) on the principles of data reduction and dimensionality reduction, are soon to find place in tele-surgery and tele-robotics ( Vinjamuri et al (2007)). Synergies are projected to be miniature windows to provide immense help in next generation rehabilitation.…”

“…Synergies are projected to be miniature windows to provide immense help in next generation rehabilitation. Recently our group has demonstrated a synergy based brain machine interface where two control signals calculated from the spectral powers of the brain signals controlled two synergies, that commanded a 10 DoF virtual hand ( Vinjamuri et al (2011)). This showed promising results for controlling a synergy-based neural prosthesis.…”

“…We have systematically investigated the use of linear dimensionality reduction methods in extracting movement primitives or synergies in hand movements in Vinjamuri et al (2010a;2011). In this chapter, we applied linear (PCA and Multidimensional Scaling (MDS)) and nonlinear (kPCA, Isomap, LLE) dimensionality reduction methods in extracting kinematic synergies in grasping tasks of the human hand.…”

Introductory Chapter: Toward Near-Natural Assistive Devices

“…Additionally, Vidaurre et al demonstrated EEG-based control of an exoskeleton [38]. Invasive brain-computer interfaces have demonstrated accurate and real-time control of advanced prosthetics such as modular prosthetic limb [15,23,40,41].…”

Simultaneous Neural Control of Simple Reaching and Grasping With the Modular Prosthetic Limb Using Intracranial EEG

“…It is thus an important target for brain-machine interfaces (BMIs) being developed for patients with impaired limb function due to neurological lesions of motor pathways (e.g., spinal cord injury, amyotrophic lateral sclerosis, stroke, etc.). Recent work has demonstrated that grasp types [1], grasp timing [2], hand postures [3], and reach parameters [4] can be decoded from spectral changes in human intracranial electroencephalographic (iEEG) signals, and that movement-related spectral modulation of iEEG can be used for online control of BMIs, for example during dexterous grasping [5], when selecting between grasp types and elbow movement [6], or for three dimensional cursor control [7]. We therefore sought to determine whether human iEEG could be used to provide simultaneous and independent online control of reaching and grasping movements, thus demonstrating segregation of these two movement types at the spatial scale of iEEG macroelectrodes.…”