Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general

By Thorsten Oliver Zander, Christian Andreas Kothe. Journal of Neural Engineering, 2011

893 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8ed32332f6cf2a4689c404b6b964774afa959ecb

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

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, neutral): “…this information together facilitates investigation and proliferation of several interesting and important questions in BCI and neuroscience in general, such as mental state estimation [59,60], multi-modal data-fusion [56,61,62], and covariance shifts of the input space [63,64], among many others.”
Deep learning-based electroencephalography analysis: a systematic review (2019, background): “Applications such as cognitive and affective monitoring are very promising as they could allow unbiased measures of, for example, an individual’s level of fatigue, mental workload, [21, 195], mood, or emotions [5].”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “untary control, resulting in an enriched human-computer interaction (HCI) with implicit information [6].”
Online and Offline Domain Adaptation for Reducing BCI Calibration Effort (2017, background): “Research [33], [39], [54] has shown tha t these BCI systems enable the subject to detect targets in lar ge aerial photographs faster and more accurately than traditi onal standard searches.”
A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Zander and Kothe proposed the concept of a passive brain-computer interface (BCI) to fuse conventional BCI systems with cognitive monitoring (Zander & Kothe 2011).”
Pay attention!: designing adaptive agents that monitor and improve user engagement (2012, background): “Current research has shown a paradigm shift towards passive systems, which implicitly monitor user conscious and uncon-scious brain activity [47].”
Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS (2014, neutral): “These so called passive Brain-Computer Interfaces (Cutrell and Tan, 2008; Zander and Kothe, 2011) mostly use the Electroencephalogram (EEG).”
Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, neutral): “, to provide communication to severely paralyzed users [3], as a new control device for gaming [4] or to design adaptive human–computer interfaces that can react to the user’s mental state [5], to name a few [6].”
Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2013, neutral): “affective Brain–Computer Interaction, see [127] and [128].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, neutral): “New EEG paradigms can also be developed to facilitate cognitive control [256] and interaction with the environment [154, 155].”
BCILAB: a platform for brain–computer interface development (2013, background): “This includes uses for communication and prosthetic control (for example, to support mobility and communication of severely disabled persons) [11,12], passive assessment of cognitive state in human-computer interfaces [13], and hybrid BCIs that fuse multiple measurement modalities, including non-bra”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, methodology): “Moreover, oscillatory activity-based BCI can be useful for a number of applications for which ERP-based BCI cannot be used, including (but not limited to) self-paced BCI control [23][24], stroke rehabilitation [25][26] or passive BCI that monitor and adapt an application based on users’ mental state”
State of science: mental workload in ergonomics (2015, background): “…to the study of MWL. Recently, with new information and communications technology, facilities of ambulant brain–computer interfaces (BCI) (cf. Zander and Kothe 2011) and brain activity measurement systems such as near-infrared spectroscopy (NIRS) have enabled accessible non-invasive…”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Although the distinction between higher-order and lowerorder functions is not clear cut, by convention BMIs are called cognitive if they work in the domains of cognitive states (348, 886), executive functions (349), decision making (21, 335, 343), memory (62, 63), attention (35, 417), and language (”
Visual and Auditory Brain–Computer Interfaces (2014, background): “hybrid BCI [20], passive BCI [21], emotional BCI [22], and collaborative BCI [23]) have appeared in various publications.”