Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
A new approach for EEG feature extraction in P300-based lie detection
Computer Methods and Programs in Biomedicine
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Event-related Potentials (ERPs) are voltage fluctuations in electroencephalogram that allow the examination of electrical representations of the underlying sensory and cognitive processes occurring in the brain in response to stimuli. These waveforms contain characteristic peaks and troughs, which can correspond to certain underlying processes. The determination of the functional significance of a particular ERP component involves simultaneous consideration of its eliciting conditions, polarity, latency and scalp distribution. The evaluation of these parameters, by medical specialists, leads to diagnostic of important psychiatric disorders such as attention deficit/hyperactivity disorder (ADHD). However, the measurement on these parameters is usually susceptible to the subjectivity of the medical concept. This work presents a comparison between two methodologies that consider characterization and feature extraction/selection of ERPs signals, in order to distinguish normal from ADHD patients on a feature set formed by morphological, frequency and wavelet characteristics. Moreover, tests are made on the raw signals looking for informative events that could provide an increasing on classification accuracy.