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EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Computer Engineering Department, Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires 1441, Argentina
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Author to whom correspondence should be addressed.
Current address: C1437FBH Lavarden 315, Ciudad Autónoma de Buenos Aires 1441, Argentina.
Brain Sci. 2018, 8(11), 199; https://doi.org/10.3390/brainsci8110199
Received: 27 September 2018 / Revised: 9 November 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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. View Full-Text
Keywords: electroencephalography; brain-computer interfaces; waveform; p300; SIFT; PE; MP; SHCC electroencephalography; brain-computer interfaces; waveform; p300; SIFT; PE; MP; SHCC
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Ramele, R.; Villar, A.J.; Santos, J.M. EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces. Brain Sci. 2018, 8, 199.

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