Abstract
The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.
Highlights
Current society is demanding technology to provide the means to realize the utopia of social inclusion for people with disabilities [1]
The digital revolution and the pervasiveness of digital gadgets have modified the way people interact with the environment through these devices [3]
At the center of all this hype, we can find a hundredth year old technology, rock-solid as a diagnosis tool, which greatly benefited from the shrinkage of sensors, the increase in computer power and the widespread development of wireless protocols and advanced electronics: the Electroencephalogram (EEG) [5]
Summary
Current society is demanding technology to provide the means to realize the utopia of social inclusion for people with disabilities [1]. The digital revolution and the pervasiveness of digital gadgets have modified the way people interact with the environment through these devices [3] All this human computer interaction is based on muscular movement [4], but these trends are pushing this boundary beyond the confines of the body and beyond the limitation of human motion. At the center of all this hype, we can find a hundredth year old technology, rock-solid as a diagnosis tool, which greatly benefited from the shrinkage of sensors, the increase in computer power and the widespread development of wireless protocols and advanced electronics: the Electroencephalogram (EEG) [5]
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