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 offline 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 modification for new datasets, brain areas and participants.

为了推断意图，脑机接口必须提取准确估计神经活动的特征。但是，信号质量随时间推移而下降，阻碍了使用特征工程技术来恢复功能信息。通过使用植入三名人类参与者皮层中的电极阵列记录的神经数据，我们在这里展示了卷积神经网络可用于在所有电极必须使用相同的神经网络参数的约束下，通过联合优化特征提取和解码，将电信号映射到神经特征。在所有三个参与者中，神经网络导致所有指标的光标控制任务的离线和在线性能得到改善，超过了宽带神经数据的阈值交叉率和小波分解率（以及其他特征提取技术）。我们还表明，经过训练的神经网络无需修改即可用于新的数据集、大脑区域和参与者。