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The ECoG decoder ( c ) using the 3D ResNet architecture. We first use several temporal and spatial convolutional layers with residual connections and spatiotemporal pooling to generate downsampled latent features, and then use corresponding transposed temporal convolutional layers to upsample the features to the original temporal dimension. We then apply temporal convolution layers and channel MLPs to map the features to speech parameters, as shown in b . The non-causal version uses non-causal temporal convolution in each layer, whereas the causal version uses causal convolution.

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