P300 brain computer interface: current challenges and emerging trends

By Reza Fazel-Rezai, Brendan Z. Allison, Christoph Guger, Eric W. Sellers, Sonja C. Kleih, Andrea Kübler. Frontiers in Neuroengineering, 2012

367 citations recorded by the source corpus.

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/56d986c576d37c2f9599cabb8ba5a59660971045

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “clinical practice by Niels Birbaumer (71), have undergone a considerable expansion (14, 74, 246, 358, 815, 863).”
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control (2013, background): “following the presentation of a visual motion stimuli, which can be used in BCIs [16], [17].”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “Indeed, ERP are usually more stable across users than EEG oscillations [13], [14], and the specific structures of ERP spellers can be exploited in a smart way to reduce or suppress the need for calibration [15], [16].”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, background): “They are generally robust across subjects and contain well-stereotyped waveforms, enabling the exact time course of the ERP to be modeled through machine learning efficiently [40].”
A High-Speed Brain Speller using steady-State Visual evoked potentials (2014, background): “11, 12 Typically, a P300-or VEP-based BCI is capable of reaching more than 30 characters for spelling.”