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Enhancing the Classification Accuracy of SSVEP based BCI using CWT method along with ANN
Published in IJAREM
Volume: 1.0
Issue: 1.0
Pages: 81.0 - 89.0
This paper reviews the application of continuous wavelet transform (CWT) with artificial neural network (ANN) for the neurological waveform detection and pattern analysis. A brain-computer interface (BCI) is a promising communication channel used to connect the brain to external electronics devices. SSVEP signals are used as basis for BCI because of its reliability, high information transfer rate, minimum training and flexibility. The continuous wavelet transform (CWT) offers a valuable tool for the analysis of the signals as it provides precise location in term of high frequency components. The selection of mother wavelet having high correlation with the signal under study provides a more accurate time frequency analysis. This paper reviews the application of the artificial neural network (ANN) along with the CWT method to the waveform detection and pattern analysis of the SSVEP signal. ANN methods are shown to be an excellent way of incorporating expert knowledge about the brain into a mathematical framework with minimal assumptions about the statistics of signals and noise.
About the journal
JournalInternational Journal of Advanced Research in Engineering & Management
Open Access0