Wavelet denoising has been successfully applied to Event-Related Potential (ERP) detection, but it usually works using channels information independently. This paper presents an adaptive approach to denoise signals taking into account the channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information to automatically select a time window which increases ERP det...

This article presents a new computational intelligence technique for pattern recognition of graphic elements (e.g. event-related potential, auditory evoked potential, kcomplex, spindle) embedded in electro-encephalographic signals. More precisely, we have extended the learning vector quantization (LVQ) algorithm by Kohonen to nonidentity assignment to robustly detect evoked potentials in a nois...

The usability of Brain Computer Interfaces (BCI) based on the P300 speller is severely hindered by the need for long training times and many repetitions of the same stimulus. In this contribution we introduce a set of unsupervised hierarchical probabilistic models that tackle both problems simultaneously by incorporating prior knowledge from two sources: information from other training subjects...

In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evaluated with a large corpus of essays written by native and nonnative speakers of English to the writing prompts of high-stakes standardized tests (TOEFL and GRE). We also present comparative evaluations with Aspell and the speller from Mic...

Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy...