Automatic detection method of P300 waveform in the single sweep records by using a neural network.
Event-related potentials (ERPs) are responses related to the recognition of certain stimuli. P300 is the most important positive component in the ERP and appears around 300 ms after the target stimulus in the oddball paradigm. In our previous work, we proposed a method for the automatic detection of the P300 waveform in single-sweep records by using a correlation technique. However, determination of the threshold values of the P300 waveform for the correlation study was not an easy task. In skirting this problem, we developed an automatic method of detecting a single-sweep P300 waveform by using an artificial neural network. We selected appropriate characteristic parameters of positive peaks as input signals for the input layer units, and the weights between the layers were determined by using the back-propagation algorithm. The neural network for P300 detection was obtained automatically, based on the data of ERPs obtained from 11 healthy males, and gave substantial accuracy for P300 detection. Furthermore, by using this neural network we clarified the way in which the P300 waveform is judged visually by each inspector.
Department of Electrical Engineering, Saga University, Japan.
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Nishida
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Nakamura
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Suwazono
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Honda
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Nagamine
H
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Shibasaki
D000161:Acoustic Stimulation; D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000379:methods; D004594:Electrophysiology / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer