@article {4495,
	title = {Brain-computer interface-based assessment of color vision.},
	journal = {J Neural Eng},
	volume = {18},
	year = {2021},
	month = {2021 Nov 26},
	abstract = {<p>Present methods for assessing color vision require the person{\textquoteright}s active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person{\textquoteright}s participation.This method uses steady-state visual evoked potentials to identify metamers-two light sources that have different spectral distributions but appear to the person to be the same color.We demonstrate that: minimization of the visual evoked potential elicited by two flickering light sources identifies the metamer; this approach can distinguish people with color-vision deficits from those with normal color vision; and this metamer-identification process can be automated.This new method has numerous potential clinical, scientific, and industrial applications.</p>},
	keywords = {brain-computer interfaces, Color Vision, Electroencephalography, Evoked Potentials, Visual, Humans, Light, Photic Stimulation, Research Design},
	issn = {1741-2552},
	doi = {10.1088/1741-2552/ac3264},
	author = {Norton, James J S and DiRisio, Grace F and Carp, Jonathan S and Norton, Amanda E and Kochan, Nicholas S and Wolpaw, Jonathan R}
}
@article {4362,
	title = {A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.},
	journal = {IEEE Trans Neural Syst Rehabil Eng},
	volume = {29},
	year = {2021},
	month = {2021},
	pages = {1766-1773},
	abstract = {<p>We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.</p>},
	keywords = {Algorithms, brain-computer interfaces, Electroencephalography, Evoked Potentials, Visual, Humans, Photic Stimulation},
	issn = {1558-0210},
	doi = {10.1109/TNSRE.2021.3106876},
	author = {Habibzadeh, Hadi and Norton, James J S and Vaughan, Theresa M and Soyata, Tolga and Zois, Daphney-Stavroula}
}
