Brain Computer Interfaces, a Review

By Luis F. Nicolás-Alonso, Jaime Gómez Gil. Italian National Conference on Sensors, 2012

1,139 citations recorded by the source corpus.

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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

View Paper: https://www.semanticscholar.org/paper/10b31d3058621d7c789cad876ed2dc2653e06b1b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Progress in Brain Computer Interface: Challenges and Opportunities (2019, methodology): “For specialized reviews on particular BCI topics we refer to the recent literature (McFarland et al., 2006; Schwartz et al., 2006; Bashashati et al., 2007; Lotte et al., 2007, 2018; Matthews et al., 2007; Sitaram et al., 2007; Mak and Wolpaw, 2009; Menon et al., 2009; Nicolelis and Lebedev, 2009; Su”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “The performance of SMR-BCIs is heavily dependent on the neurophysiological and psychological state of the user, with the control of SMR activity being found to be challenging for many users [9,23].”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “For more details on genetic algorithms, please see Pernkopf and O’Leary (2001) and Nicolas-Alonso and Gomez-Gil (2012).”
The grand challenges of Science Robotics (2018, background): “Beyond their clinical use, BCIs also have emerging applications in neuroer-gonomics, communication and control, education and self-regulation, as well as games and entertainment ( 72 ).”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, neutral): “…that exogenous BCI paradigms [38] (i.e., ERP and SSVEP), where external visual stimuli evoke brain responses, show a relatively small ratio of BCI illiteracy compared to the endogenous BCI paradigm [38] (i.e., MI) where a userinducesthebrainsignalswithapredefinedmentaltask(i.e., imaginedmovements).”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, background): “processing stages [3]: a data collection stage, where neural data is recorded; a signal processing stage, where the recorded data is preprocessed and cleaned; a feature extraction stage, where meaningful information is extracted from the neural data; a classification stage, where a decision is inter”