Abstract
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.
Highlights
Brain-computer interfaces (BCIs), systems designed to translate neural activity into a machine command to control an external assistive device, have emerged with the potential to restore function to patients suffering from conditions such as spinal cord injury, ALS, and stroke
This study demonstrates that kinematic parameters, in particular speed, velocity, and position, of 3D reaching movements can be decoded from ECoG signals in human patients
The ability to decode kinematics of reaching movements with several degrees of freedom builds upon previous demonstrations that ECoG signals can be used to decode movement trajectories (Schalk, Kubanek et al 2007, Pistohl, Ball et al 2008, Sanchez, Gunduz et al 2008, Ganguly, Secundo et al 2009, Chao, Nagasaka et al 2010, Shimoda, Nagasaka et al 2012, Marathe and Taylor 2013) and to control closed-loop brain-computer interface (BCI) (Leuthardt, Schalk et al 2004, Wilson, Felton et al 2006, Felton, Wilson et al 2007, Schalk, Miller et al 2008, Rouse and Moran 2009, Rouse, Williams et al 2013)
Summary
Brain-computer interfaces (BCIs), systems designed to translate neural activity into a machine command to control an external assistive device, have emerged with the potential to restore function to patients suffering from conditions such as spinal cord injury, ALS, and stroke. At one end of the spectrum, penetrating multi-electrode arrays have high spatial resolution and have been used to control closed-loop BCI systems in non-human primates (Wessberg, Stambaugh et al 2000, Serruya, Hatsopoulos et al 2002, Taylor, Tillery et al 2002, Carmena, Lebedev et al 2003, Velliste, Perel et al 2008) and human patients (Kennedy and Bakay 1998, Hochberg, Serruya et al 2006, Kim, Simeral et al 2008, Collinger, Wodlinger et al 2013, Wodlinger, Downey et al 2015) These systems require invasive surgical procedures with risks of damage to cortex near the implant site (Bjornsson, Oh et al 2006) and recording signal quality decreases over time due to the brain’s immune response (Williams, Hippensteel et al 2007). ECoG signals have been used to develop BCI systems in nonhuman primates (Rouse and Moran 2009, Rouse, Williams et al 2013), motor-intact human subjects (Leuthardt, Schalk et al 2004, Wilson, Felton et al 2006, Felton, Wilson et al 2007, Schalk, Miller et al 2008), and in a quadriplegic human patient (Wang, Collinger et al 2013), and are stable over several months (Chao, Nagasaka et al 2010)
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