Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients

By Abdulhamit Subasi. Expert systems with applications, 2005

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EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, methodology): “…ANN models with different architectures have been used such as multilayer perceptron neural network (MLPNN) (Alkan, Koklukaya, & Subasi, 2005; Subasi, 2005a, 2005b, 2007; Subasi & Ercelebi, 2005; Ubeyli, 2009), adaptive neuro-fuzzy inference system (ANFIS) (Guler & Ubeyli, 2005), radial…”
EEG signal classification using wavelet feature extraction and a mixture of expert model (2007, methodology): “3 (Subasi, 2005b).”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “Hence, it is broadly applied to process non-steady neural signals, especially for the EEG [79].”
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks (2006, background): “In general, it must be said that no time-frequency regions but rather time-scale regions are defined [9],[10].”
Using EEG spectral components to assess algorithms for detecting fatigue (2009, background): “During alert wakefulness (with eyes open), alpha activity is normally low, unless the individual is currently fatigued, in which state alpha will start showing higher activity (Åkerstedt and Gillberg, 1990; Kecklund and Åkerstedt, 1993; Subasi, 2005).”