Information Theoretical Analysis of Instantaneous Motor Cortical Neuron Encoding for Brain-Machine Interfaces

Sequential estimation algorithms based on spike trains for motor Brain-Machine Interfaces (BMI) require knowledge of both neuronal representation encoding of movement and movement decoding from spike train activity. In these BMIs, an instantaneous encoding estimation is necessary which is unlike the methods commonly used that are based on time windows of neural and kinematic data. An online, instantaneous encoding analysis based on information theoretic techniques is developed using the relationship between the instantaneous kinematic vector and neural firing in the motor cortex. Moreover, mutual information is utilized as a tuning criterion to provide a way to estimate the optimum time delay between motor cortical activity and the observed kinematics. More than half (58.38%) of the neurons instantaneous tuning curves display a 0.9 correlation coefficient with those estimated with the temporal kinematic vector. Unlike the windowed methods, one of the characteristics of the instantaneous model is that it works within the dynamic range of the kinematics. This paper shows that the instantaneous estimation provides better encoding when compared with the window models in real experimental data.