@article {4602,
	title = {Brain-computer interface (BCI)-based identification of congenital red-green color vision deficiencies},
	journal = {Authorea Preprints},
	year = {2025},
	abstract = {We demonstrate brain-computer interface (BCI)- based color vision (CV) assessment for the identification of congenital red-green CV deficiencies. Experiments were based on the identification of metamers{\textemdash}light sources with different spectral distributions perceived to be the same color. Metamers elicit steady-state visual evoked potentials (SSVEPs) of minimal size and are different for people with versus without CV deficiencies. Methods: Thirty-one participants (20 control (CTR), 11 color vision deficient (CVdef)) completed behaviorand BCI-based CV assessments. Experiments used a visual stimulus that alternated between a monochromatic light source (yellow; fixed luminance) and a dichromatic light source (red and green; varying luminances) at a fixed frequency. During behavior-based CV assessment, participants identified metamers by manually adjusting the dichromatic source{\textquoteright}s settings until its color matched that of the monochromatic source. During BCI-based CV assessment, participants attended a sequence of stimuli (each with a different dichromatic source setting) while electroencephalography was recorded; metamers were defined as the dichromatic source settings that minimized SSVEP size. Results: The behavior- and BCI-identified metamers were identical within each group, but different across groups (i.e., CTR and CVdef and for people with protan- versus deutan-type CV deficiencies). Automatic identification of CVdef individuals (and type of deficiency) was demonstrated using a classification analysis (93\% accuracy). Conclusion: Experiments validated BCI-based CV assessment for the identification of congenital red-green CV deficiencies. Significance: BCI-based CV assessment does not require behavioral responses and can be automated, making it suitable for people with cognitive/motor deficits. With further development, BCI-based CV assessment may enable automatic identification of many types of congenital and acquired CV deficiencies.},
	author = {Rueda-Parra, Sebastian and Caruso, Heather A and Norton, Penelope L and Gemoets, Darren E and Norton, James JS}
}
@article {4606,
	title = {Soleus H-reflex size versus stimulation rate in the presence of background muscle activity: a methodological study},
	journal = {Experimental brain research},
	volume = {243},
	year = {2025},
	pages = {215},
	abstract = {Hoffmann reflex (HR) operant conditioning (HROC) is an important intervention for neurorehabilitation. Current HROC paradigms elicit HRs at low rates (~ 0.2 Hz), minimizing rate-dependent depression (RDD). We investigated the impact of higher stimulation rates on HR size. Fifteen healthy participants maintained low background soleus electromyographic activity (EMG) while standing. Soleus HR and M-wave recruitment curves were obtained at rates of 0.2, 1, and 2 Hz twice, from which Mmax and Hmax were calculated. Seventy-five HRs were collected for each rate at a target M-wave size (~ 10 to 20\% of Mmax). HR depression was minimal at higher stimulation rates. The mean HR amplitude was reliable across the two repetitions and three rates, with high intraclass correlation coefficient (ICC) values. HROC could be performed consistently at rates up to 2 Hz with minimal HR depression. Faster rates enable more conditioning trials per session, reducing session duration and/or number, thereby potentially accelerating conditioning and reducing participant burden.},
	author = {Brangaccio, Jodi A and Gupta, Disha and Mojtabavi, Helia and Hardesty, Russell L and Hill, NJ and Carp, Jonathan S and Gemoets, Darren E and Vaughan, Theresa M and Norton, James JS and Perez, Monica A and others}
}
