03026nas a2200265   4500008004100000022001400041245016500055210006900220260001600289490000700305520208500312653002502397653002102422653001302443653001102456653002102467653002202488653002602510100002502536700001702561700002202578700002202600700002202622856011602644       2023                        eng d  a1741-255200aMethods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.0 aMethods for automated delineation and assessment of EMG response  c2023 Jul 210 v203 a<p>Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.</p>10aElectric Stimulation10aElectromyography10aH-Reflex10aHumans10aMuscle, Skeletal10aPeripheral Nerves10aRetrospective Studies1 aMcKinnon, Michael, L1 aHill, Jeremy1 aCarp, Jonathan, S1 aDellenbach, Blair1 aThompson, Aiko, K  uhttps://neurotechcenter.org/publications/2023/methods-automated-delineation-and-assessment-emg-responses-evoked01692nas a2200289   4500008004100000022001400041245006300055210006000118260001600178490000700194520079100201653003000992653001701022653002701039653003001066653001101096653001001107653002301117653002001140100002301160700002201183700002201205700002201227700002401249700002401273856010501297       2021                        eng d  a1741-255200aBrain-computer interface-based assessment of color vision.0 aBraincomputer interfacebased assessment of color vision  c2021 Nov 260 v183 a<p>Present methods for assessing color vision require the person's active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person'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>10abrain-computer interfaces10aColor Vision10aElectroencephalography10aEvoked Potentials, Visual10aHumans10aLight10aPhotic Stimulation10aResearch Design1 aNorton, James, J S1 aDiRisio, Grace, F1 aCarp, Jonathan, S1 aNorton, Amanda, E1 aKochan, Nicholas, S1 aWolpaw, Jonathan, R  uhttps://neurotechcenter.org/publications/2021/brain-computer-interface-based-assessment-color-vision