Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters.

Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.

. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis..

Stability of motor representations after paralysis.

Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers.

Credit Risk Modeling Using Transfer Learning and Domain Adaptation.

In the domain of credit risk assessment lenders may have limited or no data on the historical lending outcomes of credit applicants. Typically this disproportionately affects Micro, Small, and Medium Enterprises (MSMEs), for which credit may be restricted or too costly, due to the difficulty of predicting the Probability of Default (PD). However, if data from other related credit risk domains is available may be applied to successfully train models, e.