Enhanced Control Of A Braincomputer Interface By Tetraplegic Participants Via Neuralnetworkmediated Feature Extraction Benyamin Haghi Tyson Aflalo Spencer Kellis Charles Guan Jorge A Gamez De Leon Albert Yan Huang Nader Pouratian Richard A Andersen Azita Emami by Benyamin Haghi & Tyson Aflalo & Spencer Kellis & Charles Guan & Jorge A. Gamez De Leon & Albert Yan Huang & Nader Pouratian & Richard A. Andersen & Azita Emami instant download after payment.

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. In all three participants, the neural network led to ofine and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modifcation for new datasets, brain areas and participants.