Real-time detection of EEG spikes using neural networks

A digital signal processor based microcomputer system is designed for real-time detection of EEG spikes. The system acquires 16 channel EEGs, displays them in S second epochs, detects EEG spikes, identifies them on the screen and stores their times of occurrence all in real-time. Spike detection is achieved by a two-level neural network system analyzing 100 msec of multichannel EEG in a sliding window. In the first level, spikes are identified in individual EEG channels by 16 identical neural network modules computed in the digital signal processor. In the second level, outputs of the first level modules are integrated by a second neural network module for the final detection. Results show that neural network based EEG spike detection systems can be implemented for real-time clinical operation using current digital signal processor technology.