Editing Brain–computer interface (section)

==== SSVEP mobile EEG BCIs ====
In 2009, the NCTU Brain-Computer-Interface-headband was announced. Those researchers also engineered silicon-based [[Microelectromechanical systems|microelectro-mechanical system]] (MEMS) [[Electroencephalography#Dry EEG electrodes|dry electrodes]] designed for application to non-hairy body sites. These electrodes were secured to the headband's [[Data acquisition|DAQ]] board with snap-on electrode holders. The signal processing module measured [[Alpha wave|alpha]] activity and transferred it over [[Bluetooth]] to a phone that assessed the patients' alertness and cognitive capacity. When the subject became drowsy, the phone sent arousing feedback to the operator to rouse them.<ref>{{Citation| vauthors = Lin CT, Ko LW, Chang CJ, Wang YT, Chung CH, Yang FS, Duann JR, Jung TP, Chiou JC | display-authors = 6 |title=Wearable and Wireless Brain-Computer Interface and Its Applications |date=2009 |work=Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience| series = Lecture Notes in Computer Science | volume = 5638 |pages=741–748|publisher=Springer Berlin Heidelberg|doi=10.1007/978-3-642-02812-0_84|isbn=978-3-642-02811-3|s2cid=14515754}}</ref>

In 2011, researchers reported a cellular based BCI that could cause a phone to ring. The wearable system was composed of a four channel bio-signal acquisition/amplification [[Modular design|module]], a communication module, and a Bluetooth phone. The electrodes were placed to pick up steady state visual evoked potentials ([[Steady state visually evoked potential|SSVEPs]]).<ref name=":1">{{cite journal | vauthors = Wang YT, Wang Y, Jung TP | title = A cell-phone-based brain-computer interface for communication in daily life | journal = Journal of Neural Engineering | volume = 8 | issue = 2 | pages = 025018 | date = April 2011 | pmid = 21436517 | doi = 10.1088/1741-2560/8/2/025018 | s2cid = 10943518 | bibcode = 2011JNEng...8b5018W }}</ref> SSVEPs are electrical responses to flickering visual stimuli with repetition rates over 6&nbsp;Hz<ref name=":1" /> that are best found in the parietal and occipital scalp regions of the visual cortex.<ref>{{cite journal | vauthors = Guger C, Allison BZ, Großwindhager B, Prückl R, Hintermüller C, Kapeller C, Bruckner M, Krausz G, Edlinger G | display-authors = 6 | title = How Many People Could Use an SSVEP BCI? | journal = Frontiers in Neuroscience | volume = 6 | pages = 169 | date = 2012 | pmid = 23181009 | pmc = 3500831 | doi = 10.3389/fnins.2012.00169 | doi-access = free }}</ref><ref name=":2">{{cite book | vauthors = Lin YP, Wang Y, Jung TP | title = 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | chapter = A mobile SSVEP-based brain-computer interface for freely moving humans: The robustness of canonical correlation analysis to motion artifacts | volume = 2013 | pages = 1350–1353 | year = 2013 | pmid = 24109946 | doi = 10.1109/EMBC.2013.6609759 | isbn = 978-1-4577-0216-7 | s2cid = 23136360 }}</ref><ref>{{cite journal | vauthors = Rashid M, Sulaiman N, Abdul Majeed AP, Musa RM, Ab Nasir AF, Bari BS, Khatun S | title = Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review | journal = Frontiers in Neurorobotics | volume = 14 | pages = 25 | date = 2020 | pmid = 32581758 | pmc = 7283463 | doi = 10.3389/fnbot.2020.00025 | doi-access = free }}</ref> It was reported that all study participants were able to initiate the phone call with minimal practice in natural environments.<ref>{{cite patent | country = US | number = 20130127708 | gdate = 23 May 2013 }}</ref>

The scientists reported that a single channel [[fast Fourier transform]] (FFT) and multiple channel system [[canonical correlation analysis]] ([[Canonical correlation|CCA]]) algorithm can support mobile BCIs.<ref name=":1" /><ref name=":3">{{cite book | vauthors = Wang YT, Wang Y, Cheng CK, Jung TP | title = 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | chapter = Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI | volume = 2013 | pages = 5271–5274 | year = 2013 | pmid = 24110925 | doi = 10.1109/EMBC.2013.6610738 | isbn = 978-1-4577-0216-7 | s2cid = 14324159 }}</ref> The CCA algorithm has been applied in experiments investigating BCIs with claimed high accuracy and speed.<ref>{{cite journal | vauthors = Bin G, Gao X, Yan Z, Hong B, Gao S | title = An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method | journal = Journal of Neural Engineering | volume = 6 | issue = 4 | pages = 046002 | date = August 2009 | pmid = 19494422 | doi = 10.1088/1741-2560/6/4/046002 | bibcode = 2009JNEng...6d6002B | s2cid = 32640699 }}</ref> Cellular BCI technology can reportedly be translated for other applications, such as picking up sensorimotor [[Mu wave|mu]]/[[Beta wave|beta]] rhythms to function as a motor-imagery based BCI.<ref name=":1" />

In 2013, comparative tests performed on [[Android (operating system)|Android]] cell phone, tablet, and computer based BCIs, analyzed the power [[Spectral density|spectrum density]] of resultant EEG SSVEPs. The stated goals of this study were to "increase the practicability, portability, and ubiquity of an SSVEP-based BCI, for daily use". It was reported that the stimulation frequency on all mediums was accurate, although the phone's signal was not stable. The amplitudes of the SSVEPs for the laptop and tablet were reported to be larger than those of the cell phone. These two qualitative characterizations were suggested as indicators of the feasibility of using a mobile stimulus BCI.<ref name=":3" />

One of the difficulties with EEG readings is susceptibility to motion artifacts.<ref>{{cite journal | vauthors = Symeonidou ER, Nordin AD, Hairston WD, Ferris DP | title = Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion | journal = Sensors | volume = 18 | issue = 4 | pages = 1073 | date = April 2018 | pmid = 29614020 | pmc = 5948545 | doi = 10.3390/s18041073 | doi-access = free | bibcode = 2018Senso..18.1073S }}</ref> In most research projects, the participants were asked to sit still in a laboratory setting, reducing head and eye movements as much as possible. However, since these initiatives were intended to create a mobile device for daily use,<ref name=":3" /> the technology had to be tested in motion. In 2013, researchers tested mobile EEG-based BCI technology, measuring SSVEPs from participants as they walked on a treadmill. Reported results were that as speed increased, SSVEP detectability using CCA decreased. [[Independent component analysis]] (ICA) had been shown to be efficient in separating EEG signals from noise.<ref>{{cite journal | vauthors = Wang Y, Wang R, Gao X, Hong B, Gao S | title = A practical VEP-based brain-computer interface | journal = IEEE Transactions on Neural Systems and Rehabilitation Engineering | volume = 14 | issue = 2 | pages = 234–239 | date = June 2006 | pmid = 16792302 | doi = 10.1109/TNSRE.2006.875576 }}</ref> The researchers stated that CCA data with and without ICA processing were similar. They concluded that CCA demonstrated robustness to motion artifacts.<ref name=":2" /> EEG-based BCI applications offer low spatial resolution. Possible solutions include: EEG source connectivity based on [[graph theory]], EEG pattern recognition based on Topomap and EEG-[[fMRI]] fusion.