By Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani
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Additional resources for Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously
Multi-domain feature extraction for small event-related potentials through nonnegative multi-way array decomposition from low dense array EEG. International Journal of Neural Systems, 23(2(1350006)), 1–18. 1142/S0129065713500068. , Phan, A. , & Cichocki, A. (2010). Classifying Healthy Children and Children with Attention Deficit through Features Derived from Sparse and Nonnegative Tensor Factorization Using Event-related Potential. In V. Vigneron et al. ). LVA/ICA 2010, Lecture Notes in Computer Science, 6365, 620–628.
X. (2014). Analyzing Neural Time Series Data: Theory and Practice. Cambridge, MA: The MIT Press. , Nandi, A. , . . Ristaniemi, T. (2013). Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. IEEE Transactions on Multimedia, 15(5), 1060–1069. , & Ristaniemi, T. (2011). Determining the Number of Sources in High-density EEG Recordings of Event-related Potentials by Model Order Selection. Proceedings of IEEE Workshop on Machine Learning for Signal Processing (MLSP) 2011, Beijing, China, September 18–21, 1–6.
T − 1) is expressed as follows: 1 CWT(a, b) = √ |a| T −1 x(t)ψ t =0 t −b , a (2-8) where x(t) is the signal to be transformed and a and b are the socalled scaling (reciprocal of the frequency) and time location or shifting ) parameters, respectively. Therefore, ψ(t) is the mother wavelet, and ψ( t −b a is the shifted and scaled wavelet. 5 shows one example. Because a and b can be defined using any possible rational numbers, calculating the wavelet coefficients becomes very computationally expensive.
Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously by Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani