Sunday, September 21, 2014


Donoho and Johnstone propose a method for reconstructing an unknown function f on brain signal  [0,1] from noisy data di=f(ti )+σzi, i=0, …, n-1,ti=i/n, where the zi are independent and identically distributed standard Gaussian random variables. The reconstruction fˆ*n is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount σ·√(2log (n)/n).


Induced Fear via Bone Conductive Hearing to foster P300 Adaptive  Wavelet Deconstruction of Brain Signals from  Brain Computer Interface "system/participants"

Brian Computer Interface (BCI) is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. EEG separation into target and non-target ones based on presence of P300 signal is of difficult task mainly due to their natural low signal to noise ratio, a new algorithm is introduced to enhance EEG signals and improve their Signal to Noise Ratio. Our denoising method is based on multi-resolution analysis via Independent Component Analysis (ICA) Fundamentals a combination of negentropy as a feature of signal and sub-band information from evoked or "self instigated " wavelet transformation.


Gabor and wavelet (super)frames with Hermite and Laguerre functions
Luis Daniel Abreu CMUC, University of Coimbra, Portugal.

Superframes are vector-valued versions of frames which provide a tool for sending several signals into a single channel (Multiplexing). They have a rich mathematical structure which has been thoroughly investigated in the last decade.

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