The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is limited by noisy, non-stationary brain signals and high inter-user variability. We investigate the trade-off between training data quality and quantity on P300 BCI performance in individuals with amyotrophic lateral sclerosis (ALS) with representative traditional machine learning (stepwise linear discriminant analysis, SWLDA) and deep learning (EEGNet) models. Results show that data quality and domain alignment are more critical than dataset size: user-specific models trained on significantly less data outperformed generic models; generic models trained on ALS data outperformed models trained on non-ALS data; block-averaging of features was mostly detrimental to EEGNet but beneficial to SWLDA; and accounting for inter-stimulus interval differences between ALS and non-ALS data had minimal effect. Our findings highlight the importance of individualized model tuning for reliable P300 BCIs.

This paper explores a novel approach to dexterous manipulation, aimed at levels of speed, precision, robustness, and simplicity suitable for practical deployment. The enabling technology is a Direct-drive Hand (DDHand) comprising two fingers, two DOFs each, that exhibit high speed and a light touch. The test application is the dexterous manipulation of three small and irregular parts, moving them to a grasp suitable for a subsequent assembly operation, regardless of initial presentation. We employed four primitive behaviors that use ground contact as a “third finger”, prior to or during the grasp process: pushing, pivoting, toppling, and squeeze-grasping. In our experiments, each part was presented from 30 to 90 times randomly positioned in each stable pose. Success rates varied from 83% to 100%. The time to manipulate and grasp was 6.32 seconds on average, varying from 2.07 to 16 seconds. In some cases, performance was robust, precise, and fast enough for practical applications, but in other cases, pose uncertainty required time-consuming vision and arm motions. The paper concludes with a discussion of further improvements required to make the primitives robust, eliminate uncertainty, and reduce this dependence on vision and arm motion.