Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …

The recognition of spoken language has typically been studied by focusing on either words
or their constituent elements (for example, low-level features or phonemes). More recently …

Considerable progress has recently been made in natural language processing: deep
learning algorithms are increasingly able to generate, summarize, translate and classify …

Reconstructing intended speech from neural activity using brain-computer interfaces holds
great promises for people with severe speech production deficits. While decoding overt …

Speech perception is thought to rely on a cortical feedforward serial transformation of
acoustic into linguistic representations. Using intracranial recordings across the entire …

The streams of sounds we typically attend to abound in acoustic regularities. Neural
entrainment is seen as an important mechanism that the listening brain exploits to attune to …

Technology that translates neural activity into speech would be transformative for people
who are unable to communicate as a result of neurological impairments. Decoding speech …

A core goal of auditory neuroscience is to build quantitative models that predict cortical
responses to natural sounds. Reasoning that a complete model of auditory cortex must solve …

From sequences of speech sounds, or letters, humans can extract rich and nuanced
meaning through language. This capacity is essential for human communication. Yet …

Several deep neural networks have recently been shown to generate activations similar to
those of the brain in response to the same input. These algorithms, however, remain largely …