Multiple Channel Electrooculogram Classification using Automata

In this paper, we present a simple and novel technique for classification of multiple channel electrooculogram signals (EOG). In particular, a viable real-time EOG signal classifier is proposed. The classifier is based on deterministic finite automata (DFA). The system is capable of classifying sixteen different EOG signals and can be used universally for development of hardwired (using VHDL, FPGA etc.), or embedded (using microcontroller etc.) devices requiring EOG as a primary source of input. The viability of the system was tested by performing online experiments with able bodied subjects.