论文信息 - Artificial neural networks in the classification of EEG signals

Artificial neural networks in the classification of EEG signals

Analysis and interpretation of ElectroEncephaloGram (EEG) signals have found a wide spectrum of applications in clinical diagnosis. In spite of the outstanding experience of specialists, the analysis of biomedical data encounters many difficulties. Problems are associated with both technical aspects and nonstationary character of EEG sequences. Hardware and software solutions in this area are subjected to the continuous improvement due to the technological development. A very promising tool in analysis and interpretation of EEG signals are artificial neural networks. Artificial neural networks, due to their many interesting properties, are widely used to: classification, decision making, technical and medical diagnosis, pattern recognition or signal processing. The paper presents the application of artificial neural networks along with the discrete wavelet transform to the analysis and classification of neurological disorders based on recorded EEG signals.

Grzegorz Rutkowski

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