Neurology Commons^™

Empirical Models Of Scalp-Eeg Responses Using Non-Concurrent Intracranial Responses, Komalpreet Kaur, Jerry J. Shih, Dean J. Krusienski

Electrical & Computer Engineering Faculty Publications

Objective- This study presents inter-subject models of scalp-recorded electroencephalographic (sEEG) event-related potentials (ERPs) using intracranially recorded ERPs from electrocorticography and stereotactic depth electrodes in the hippocampus, generally termed as intracranial EEG (iEEG).

Approach- The participants were six patients with medically-intractable epilepsy that underwent temporary placement of intracranial electrode arrays to localize seizure foci. Participants performed one experimental session using a brain-computer interface matrix spelling paradigm controlled by sEEG prior to the iEEG electrode implantation, and one or more identical sessions controlled by iEEG after implantation. All participants were able to achieve excellent spelling accuracy using sEEG, four of the participants …

Direct Classification Of All American English Phonemes Using Signals From Functional Speech Motor Cortex, Emily M. Mugler, James L. Patton, Robert D. Flint, Zachary A. Wright, Stephan U. Schuele, Joshua Rosenow, Jerry J. Shih, Dean J. Krusienski, Marc W. Slutzky

Electrical & Computer Engineering Faculty Publications

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 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 …