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We introduce a novel, transient model for the electroencephalogram (EEG) as the noisy addition of linear filters responding to trains of delta functions. We set the synthesis part as a parameter-tuning problem and obtain synthetic EEG-like data that visually resembles brain activity in the time and frequency domains. For the analysis counterpart, we use sparse approximation to decompose the signal in relevant events via Matching Pursuit.

Affective states classification has become an important part of the Human-Computer Interface (HCI) study. In recent years, studies of physiological signals, such as ECG, GSR and EEG on affective expression have shown very promising results. In this study, we carried out two experiments to better understand the neurological expression of emotions through the use of EEG signals. In particular, we carried out a subject- independent affective states classification study using narrow- band spectral power of the EEG signals.

The Hilbert Huang Transform (HHT) has been used extensively in the time-frequency analysis of electroencephalography (EEG) signals and Brain-Computer Interfaces. Most studies utilizing the HHT for extracting features in seizure prediction have used intracranial EEG recordings. Invasive implants in the cortex have unknown long term consequences and pose the risk of complications during surgery. This added risk dimension makes them unsuitable for continuous monitoring as would be the requirement in a Body Area Network.