Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.
['David A. Friedenberg', 'Chad E. Bouton', 'Nicholas V. Annetta', 'Nicholas Skomrock', 'Mingming Zhang', 'Michael Schwemmer', 'Marcia Bockbrader', 'W. Jerry Mysiw', 'Ali R. Rezai', 'Herbert S. Bresler', 'Gaurav Sharma']
Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface