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


METHODS
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.


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.


CONCLUSIONS
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.
Bioengineering, University of Illinois at Chicago, 851 S. Morgan Street, Chicago, IL 60607, USA.
Emily M
EM
Mugler
James L
JL
Patton
Robert D
RD
Flint
Zachary A
ZA
Wright
Stephan U
SU
Schuele
Joshua
J
Rosenow
Jerry J
JJ
Shih
Dean J
DJ
Krusienski
Marc W
MW
Slutzky
D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D007802:Language; D008297:Male; D009044:Motor Cortex; D010363:Pattern Recognition, Automated / Q000379:methods; D013018:Sound Spectrography / Q000379:methods*; D013060:Speech / Q000502:physiology*; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D049250:Speech Recognition Software; D014175:Translating; D014481:United States; D014584:User-Computer Interface