Abstract

Presents the algorithm and implementation of real-time feature extraction and pattern recognition for signals from a cultured living neural-cell network. Feature extraction and pattern recognition are achieved by the application of data compression techniques and an artificial neural network (ANN). The implementation consists of a 80386-based PC and a TMS320C30 digital signal processing (DSP) card that is inserted into an expansion slot in the PC. Off-line training of the ANN is done in the PC. Real-time recognition of the pulse patterns from the living neural cells is done in the DSP sub-system. The recognition results are sent from the DSP subsystem to the PC in real-time for display and recording. The feature extraction technique is introduced. The training system and the recognition system are described. >

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