The Hybrid BCI

By Gert Pfurtscheller, Brendan Z. Allison, Clemens Brunner, G. Bauernfeind, Teodoro Solis-Escalante, Reinhold Scherer, Thorsten Oliver Zander, G. Mueller-Putz, Christa Neuper, Niels Birbaumer. Frontiers in Neuroscience, 2010

760 citations recorded by the source corpus.

Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ee51ca64379ef251ffc6afaa27836721c07822c3

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, neutral): “BCI is reported to offer the highest classification accuracy than any of its individual component alone [110].”
Control strategies for active lower extremity prosthetics and orthotics: a review (2015, neutral): “However, EEG signals could be combined with other sensory inputs in the framework of the so-called hybrid BCIs [51,52] in order to decode user’s high-level commands more reliably.”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “overt behaviors, for example, eye movements or EMGs, are called hybrid BMIs (337, 387, 476, 626, 833).”
Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general (2011, methodology): “A BCI deﬁnes a new input modality for human–machine interaction which could substitute or add up to [26] other input modalities like manual input.”
Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review (2017, neutral): “VIII) BCI Hybridization , that is, the combination of several BCI modalities on the same interface to increase the accuracy, the ergonomy, usability and the bit rate (e.g., [49-51]).”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “More details of classiﬁers, applications, and issues regarding BCI systems can be seen in several reviews of BCI [3], [4], [66], [90]–[94], [137].”
P300 brain computer interface: current challenges and emerging trends (2012, methodology): “Some groups have begun developing “hybrid” BCI systems (Pfurtscheller et al., 2010; Allison et al., 2012), which may combine a P300 BCI with another BCI.”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, neutral): “In hybrid paradigms, sequential or simultaneous processing structures can be used to output control commands to the BCI system [157].”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, background): “It could be realized by simply adding another visual stimulus [11] or combining SSVEPs with other EEG features [37] to control the on/off states of BCI.”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “In the second approach, the signals from the different interfaces are considered sequentially [59], as in the case of [61], where the NIRs system was used to ﬂag the occurrence of a MI event, and the EEG signals were used to classify the event.”