TY - JOUR
T1 - Decoding three-dimensional reaching movements using electrocorticographic signals in humans
AU - Bundy, David T.
AU - Pahwa, Mrinal
AU - Szrama, Nicholas
AU - Leuthardt, Eric C.
N1 - Publisher Copyright:
© 2016 IOP Publishing Ltd.
PY - 2016/2/23
Y1 - 2016/2/23
N2 - Objective. Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space. Approach. To investigate this, we designed a 3D center-out reaching task that was performed by five epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares (PLS) regression model to perform offline prediction of hand speed, velocity, and position. Main Results. The hierarchical PLS regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters. Significance. We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.
AB - Objective. Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space. Approach. To investigate this, we designed a 3D center-out reaching task that was performed by five epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares (PLS) regression model to perform offline prediction of hand speed, velocity, and position. Main Results. The hierarchical PLS regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters. Significance. We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.
KW - BCI
KW - ECoG
KW - brain-computer interface
KW - electrocorticography
KW - neuroprosthetics
UR - http://www.scopus.com/inward/record.url?scp=84962359268&partnerID=8YFLogxK
U2 - 10.1088/1741-2560/13/2/026021
DO - 10.1088/1741-2560/13/2/026021
M3 - Article
C2 - 26902372
AN - SCOPUS:84962359268
SN - 1741-2560
VL - 13
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 2
M1 - 026021
ER -