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  "text": "Let's dive deeper into each component.\n\nThe Encoder itself consists of two stages: a \"BrainModule\" that is a convolutional feature extractor, and a Conformer model.\n\nThis Conformer outputs a temporal MEG embedding. This MEG embedding is then mapped to discrete 28-character predictions with a linear layer. It is trained with a Connectionist Temporal Classification (CTC) objective which learns to align variable length MEG data to keystroke sequences without needing the exact timing.\n\nTherefore, the Encoder outputs both an MEG embedding and character-level predictions.",
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