Fully complex magnetoencephalography

Fully complex magnetoencephalography.

Complex numbers appear naturally in biology whenever a system can be analyzed in the frequency domain, such as physiological data from magnetoencephalography (MEG). For example, the MEG steady state response to a modulated auditory stimulus generates a complex magnetic field for each MEG channel, equal to the Fourier transform at the stimulus modulation frequency. The complex nature of these da...

Fully complex extreme learning machine

Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, ...

Magnetoencephalography

To cite: Proudfoot M, Woolrich MW, Nobre AC, et al. Pract Neurol 2014;14:336–343. INTRODUCTION The understanding of brain function is moving rapidly towards a systems-level, network-based approach. It is now as naive to talk simplistically about what a particular area of the brain ‘does’, as it was for Franz Joseph Gall (1758–1828) to link its performance to the thickness of the overlying skull...

Approximation by Fully Complex Multilayer Perceptrons

We investigate the approximation ability of a multilayer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville's theorem. To avoid the conflict between the boundedness and the analyticity ...

15 Representations of fully complex functions