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In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnbot.2017.00035">doi:10.3389/fnbot.2017.00035</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28790910">pmid:28790910</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5522881/">pmcid:PMC5522881</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wcg2mc32mvei3mtvsyropxbzim">fatcat:wcg2mc32mvei3mtvsyropxbzim</a> </span>
more »... scopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided. iNTRODUCTiON Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) endow braincomputer interfaces (BCIs) with their essential and indispensable attributes of non-invasiveness, low cost, and portability. EEG-and fNIRS-based BCIs have enabled paralyzed patients to communicate and control external devices with their own brain functions. FigURe 5 | Electroencephalography (EEG)-NIRS-based brain-computer interface: the figure shows a method of removal of false-positive motor imagery signals in EEG data using functional near infrared spectroscopy (fNIRS) (delayed decision). 8 Hong and Khan Hybridization in BCI Frontiers in Neurorobotics | www.frontiersin.org
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