Direct classification of all American English phonemes using signals from functional speech motor cortex

摘要

Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or success of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity that is distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words/min), supporting pursuit of speech articulation for BCI control.