Connectionist temporal classification (CTC)
Connectionist temporal classification (CTC) is a loss and decoding scheme that lets a recurrent or convolutional network map an input sequence to a shorter output sequence (for example, neural activity to a string of phonemes or characters) without a frame-by-frame alignment between the two. It introduces a blank symbol and sums the probability over all alignments that collapse to the target label, so the network learns where symbols occur rather than being told. This resolved a central obstacle in speech and handwriting BCI, where the mapping from continuous cortical activity to discrete units is not pre-segmented.
CTC assumes the outputs are conditionally independent given the input, so it is typically combined with an external language model during beam-search decoding to enforce lexical and grammatical structure — the language-model-in-the-loop that raised handwriting and speech BCI accuracy to communication-usable rates.
In a handwriting BCI, a recurrent network reads motor-cortex activity as the user attempts to write letters and, trained with CTC, emits a character stream without anyone labeling which neural samples correspond to which stroke; a language-model beam search then corrects the raw output into fluent text.
CTC lets the network learn the alignment between continuous cortical activity and discrete characters on its own.