Role of spatial representations, muscle force and joint angle in decoding hand kinematics from non-invasive electroencephalographic signals

Akella Ashlesha, A.

Role of spatial representations, muscle force and joint angle in decoding hand kinematics from non-invasive electroencephalographic signals

Files

Ashlesha Akella_MA_2013.pdf (2.69 MB)

Authors

Akella Ashlesha, A.

Issue Date

2013-08-05

Language

en

URI

http://theses.ubn.ru.nl/handle/123456789/203

Abstract

Bradberry et al. [Bradberry, Gentili, Contreras; 2010] has shown the possibility of noninvasively decoding hand kinematics during three-dimensional (3D) centre-out reaching task, using neural data acquired from electroencephalographic (EEG) recordings. In their experiment, primary sensorimotor cortex and inferior parietal lobule are identified as the major sources in estimating hand velocity. Activity in neurons of primary motor cortex is related to the muscle force required to produce a movement and direction of movement at the joints. Inferior parietal lobe (IPL) is concerned with “where” objects are in the environment. However, it is unclear how Bradberry‟s decoder is decoding hand velocity from these representations; spatial position, muscle force and joint angle representations. Our research aims to understand the relative usefulness of these representations in decoding hand kinematics. We used similar experimental setup as Bradberry‟s et.al and analyzed each representation separately. The inherent correlation between given finger-tip trajectory (position representation) and muscle force is removed by attaching weight to the arm. The correlation between joint angle and finger-tip trajectory is removed by maintaining a limb orientation during the movement. A similar decoding method as Bradberry‟s method is used in the project; Multivariable Linear decoding model (MLD). Three decoders are trained; position, muscle force and joint angle decoder to predict finger-tip trajectory, muscle force exerted and rotation angles at the joint respectively and their performances compared. We found that position and muscle force decoders performed significantly above chance with the average correlation over 7 participants peaking at 0.75 for position decoder and 0.51 for muscle force decoder. The joint angle decoder showed no correlation between measured and estimated angles. This implies that Bradberry‟s velocity decoder might be combining position and muscle force representation in decoding hand velocities. MLD is linear decoding model which can learn linear transformation. Velocity is a linear transformation of position. To analyze if Bradberry‟s velocity decoder is predicting velocities by making linear transformation of position representation, we further analyzed position, velocity and acceleration measures. We found that MLD decoder trained to predict positions, velocities and accelerations showed decreasing performances (i.e. position performance > velocity performance > acceleration performance). This implies that position is the simple representation in EEG data, which Bradberry‟s decoder might be using to indirectly predict velocities.