Journal
JOURNAL OF NEURAL ENGINEERING
Volume 11, Issue 3, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1741-2560/11/3/035015
Keywords
electrocorticography; speech production; phonemes; linear discriminant analysis; brain-computer interface
Categories
Funding
- Doris Duke Charitable Foundation [2011039]
- National Science Foundation [0549489, 0718558, 1064912]
- National Institutes of Health [NIBIB/NINDS EB00856]
- Mayo Clinic Foundation CR20 Grant
- Direct For Education and Human Resources
- Division Of Graduate Education [0549489] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0718558] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1064912] Funding Source: National Science Foundation
Ask authors/readers for more resources
Objective. Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we sought to decode elements of speech production using ECoG. Approach. We investigated words that contain the entire set of phonemes in the general American accent using ECoG with four subjects. Using a linear classifier, we evaluated the degree to which individual phonemes within each word could be correctly identified from cortical signal. Main results. 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. Significance. 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(-1) (33.6 words min(-1)), supporting pursuit of speech articulation for BCI control.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available