An overview of optimal signal processing methodologies for brain machine interfaces
We are part of a multi-university consortium headed by Duke University with the goal of developing Brain Machine Interfaces (BMIs). Hundreds of electrodes are surgically implanted in the motor cortex of monkeys and live sessions of the behaving animals are simultaneous recorded with the position of the hand in 3D space. Our goal is to derive optimal models to translate the spike train data into the hand position, using a “black box” system identification framework. This task poses a new set of problems for optimal signal processing because of the sheer dimensionality of the input and its discrete nature (spike trains) compounded with sampling noise eventual nonlinearity and nonstationarity. In this presentation we will review our approaches to develop optimal input-output models based on HMMs, Kalman and Wiener filters and recurrent neural networks. Furthermore, we will quantify the performance of the recurrent neural network and from the trained model, infer the importance of the neurons and how the mapping was achieved.