Realtime bioelectrical data acquisition and processing from 128 channels utilizing the wavelet-transformation

Epileptic seizure detection using dynamic wavelet network

“…A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications (Adeli, Zhou, &, Dadmehr, 2003;Basar, Schurmann, Demiralp, Basar-Eroglu, & Ademoglu, 2001;Folkers, Mosch, Malina, & Hofmann, 2003;Geva, & Kerem, 1998;Hazarika, Chen, Tsoi, & Sergejer, 1997;Kalayci, & Ozdamar, 1995;Khan, & Gotman, 2003;Patwardhan, Dhawan, & Relue, 2003;Petrosian, Prokhorov, Homan, Dashei, & Wunsch, 2000;Quiroga, Sakowitz, Basar, & Schurmann, 2001;Quiroga, & Schurmann, 1999;Rosso, Blanco, & Rabinowicz, 2003;Rosso, Martin, & Plastino, 2002;Samar, Bopardikar, Rao, & Swartz, 1999;Soltani, Simard, & Boichu, 2004;Zhang, Kawabata, & Liu, 2001). It should also be emphasized that the WT is appropriate for analysis of non-stationary signals, and this represents a major advantage over spectral analysis.…”

EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

“…Petrosian et al [6] showed that the ability of specifically designed and trained recurrent neural networks (RNN) combined with wavelet preprocessing, to predict the onset of epileptic seizures both on scalp and intracranial recordings only one-channel of electroencephalogram. In order to provide faster and efficient algorithm, Folkers et al [7] proposed a versatile signal processing and analysis framework for bioelectrical data and in particular for neural recordings and 128-channel EEG. Within this framework the signal is decomposed into sub-bands using fast wavelet transform algorithms, executed in real-time on a current digital signal processor hardware platform.…”

Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction

“…Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied. This approach is based on earlier observations that the EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands-delta (1-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12)(13), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Such methods have proved beneficial for various EEG characterizations, but fast Fourier transform (FFT), suffer from large noise sensitivity.…”

“…Petrosian et al [6] showed that the ability of specifically designed and trained recurrent neural networks (RNN) combined with wavelet pre-processing, to predict the onset of epileptic seizures both on scalp and intracranial recordings only one-channel of electroencephalogram. In order to provide faster and efficient algorithm, Folkers et al [7] proposed a versatile signal processing and analysis framework for bioelectrical data and in particular for neural recordings and 128-channel EEG. Within this framework the signal is decomposed into sub-bands using fast wavelet transform algorithms, executed in real-time on a current digital signal processor hardware platform.…”