Real-time digital implementation of a principal component analysis algorithm for neurons spike detection

This paper presents the result of a multidisciplinary experiment where electrical activity from a cultured rat hippocampi neuronal population is detected in real time by a FPGA implemented digital circuit. State-of-the-art EOMOSFET Multi Electrode Array (MEA) biosensors exploits a capacitive coupling between the biological environment and the sensing electronics to minimize invasiveness and cell damage, at the price of a lower SNR. For this reason, they are typically improved by noise rejection algorithms. Real time neural spikes detection opens unthinkable scenarios, allowing to stimulate single neurons in response to their behavior, possibly improving medical conditions like epilepsy. In this scenario, a spike sorting algorithm has been hardware implemented, allowing real time neural spike detection with a latency of 165ns.