B.M.Y. designed the experiments and interpreted the results. A.D.D. performed the experiments with input from W.E.B. W.E.B. and B.M.Y. designed the stabilization method. A.D.D. and W.E.B. developed the realtime implementation of the stabilized BCI. A.D.D. and W.E.B. performed the analyses and wrote the manuscript. E.R.O., E.C.T.-K. and A.D.D. implanted the electrode arrays used for the experiments. All authors provided feedback on the manuscript.

This article provides a review of available information on the chemistry, environmental toxicology, and mammalian toxicology of nitrilotriacetic acid (NTA). The ability of NTA to chelate metal ions such as Mg++ and Ca++ into water soluble complexes makes NTA useful as an additive to boiler water, as a builder in laundry detergents, and as a stabilizer in textile, paper, and pulp processing. Environmental fate studies show NTA biodegrades in wastewater treatment plants, in natural waters, and in soils under a wide variety of conditions. Studies on the environmental effects of NTA indicate that no adverse effects occur in treatment plants or receiving waters at anticipated levels. Monitoring programs have established that only low steady-state concentrations of NTA occur in natural waters as a result of NTA usage. In mammalian systems, NTA is not metabolized and is excreted rapidly by filtration in the kidney. No reproductive, teratogenic, or adverse bone effects have been observed at highly exaggerated doses. In numerous genotoxicity assay systems, both in vivo and in vitro, NTA is nongenotoxic. Chronic oral exposure of rodents to high doses of NTA is associated with tumorigenicity in, and restricted to, the urinary tract. The urinary tract tumors are the consequence of chronic toxicity that is caused by changes in Zn and Ca distributions between the urinary tract tissues and urine at high doses of NTA. Thresholds for the effects of NTA on Zn and Ca distributions are 10(5) to 10(6) greater than the possible maximum human exposure resulting from the low levels of NTA that are known to occur in the environment.

Objective Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a ~15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed loop setting.

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The purpose of this study was to analyze selected characteristics of high school teachers who were identified as successful by intellectually gifted high achieving students, and to discover what differentiated these teachers from teachers not so identified. More specifically, the study was concerned with personal and social traits and behaviors, professional attitudes and educational viewpoints, and classroom behavior patterns of effective teachers of gifted high school students.

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We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. The VIE is a software environment that modularizes the core functions of a neural prosthetic system--receiving signals, decoding signals and controlling a real or simulated device. Complete prosthetic systems can be quickly assembled by linking pre-existing modules together through standard interfaces. Systems can be simulated in real-time, and simulated components can be swapped out for real hardware. This paper is the first of two companion papers that describe the VIE and its use. In this paper, we first describe the architecture of the VIE and review implemented modules. We then describe the use of the VIE for the real-time validation of neural decode algorithms from pre-recorded data, the use of the VIE in closed loop primate experiments and the use of the VIE in the clinic.

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (microECoG) epidurally so that it covered the UEA. The microECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and microECoG signals were recorded from wired head-stage connectors for the UEA and microECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and microECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable.