Somayeh B. Shafiei, Ehsan T. Esfahani
¹University at Buffalo SUNY, ²University at Buffalo SUNY
In this research, we provide evidences suggesting a positive correlation between cognitive workload estimated from EEG activities and affine velocity estimated from the kinematic of the motion. Continuous hand movement is a combination of small primitive motion (segments) that connected sequentially together. To follow a specific path in space, CNS chooses a speed profile that minimizes the jerk in each segment. In other words it ensures the smoothness of hand movement by minimizing the higher order derivatives of motion. This constrained optimization quantifies a linear intrinsic relationship between the angular velocity and “2/3 power” of the curvature along the path. The velocity gain representing the slope of this linear relationship is known as equi-affine velocity.
We asked 13 subjects to draw two dimensional shapes on a touch screen platform. Brain activity of each subject was recorded using a wireless EEG acquisition headset at 9 different channels while his hand motion was recorded on the touch screen system at the sampling rate of 256Hz. We uses the constant affine velocity constrain to segment the hand movement. EEG data synchronized with each segment was then used in a supervised machine learning algorithm to estimate the level of workload. For all subjects a strong positive correlation (corr=0.28, p-vale=1.7e-6) between affine velocity and workload was observed. This finding suggests the possibility of estimating the cognitive work using the kinematics of hand movement and in particular affine velocity in 2/3rd power law.
Key Words: Workload, Motor control, 2/3rd Power Law