Quick Tips for P300 Detection

As most of you may know P300 is one of the most studied and used event related potentials (ERP) in brain computer interfaces (BCI). This potential is usually elicited by the oddball paradigm in which series of low-probability target items are mixed with high-probability non-target ones. The person carrying out the BCI task is asked […]

Where to find available EEG data?

With our previous posts, we have been trying to provide our readers with a complete set of knowledge and tools for working with EEG and other physiological signals. We presented the basics of EEG signal processing, the practical usage of EEG signal processing related to emotions or Alzheimer, useful technologies like machine learning, practical guidelines for […]

The Dark Room

One of the most interesting theories of brain function in recent years is that of surprise minimisation. This theory was born of computational neuroscience which brings together aspects of physics, information theory and machine learning and tells us that your brain spends its time predicting what will happen next and then updating predictions based on […]

Machine learning applied to affective computing (or how to teach a machine to feel)

In our last rendezvous on, ‘Affective computing (or how your PC will know how you feel)‘, I introduced different methodologies for emotion detection that configure the state- of- the- art. Methodologies for emotion detection from face expressions and from body gestures were exposed. Also methodologies from physiological signals, including EEG, galvanic skin response, heart rate […]

How To Understand The Machine Learning Family

In former posts I have given a quick-and-dirty overview of Machine Learning and related methodologies: “what most people call machine learning in neuroscience academic texts is known in other research contexts as pattern recognition, data mining, artificial intelligence (AI), computational intelligence (CI), soft computing (SC), or data analytics. All of these refer to more or […]