Paper Info
Title
A Novel Eeg-Based Four-Class Linguistic Bci
Abstract
In this work, we present a novel EEG-based Linguistic BCI, which uses the four phonemic structures "BA", "FO", "LE", and "RY" as covert speech task classes. Six neurologically healthy volunteers with the age range of 19-37 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. The duration of each trial is 312ms starting with the cue. The BCI was trained using a mixed randomized recording run containing 15 trials per class. The BCI is tested by playing a simple game of "Wack a mole" containing 5 trials per class presented in random order. The average classification accuracy for the 6 users is 82.5%. The most valuable features emerge after Auditory cue recognition (similar to 100ms post onset), and within the 70-128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke's area (linked to Phonological code retrieval), the right IFG, and Broca's area (linked to syllabification). In this work, we have only scratched the surface of using Linguistic tasks for BCIs and the potential for creating much more capable systems in the future using this approach exists.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856644
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Task analysis,Computer science,Prefrontal cortex,Brain–computer interface,Phonetics,Covert,Syllabification,Stimulus (physiology),Linguistics,Electroencephalography
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Amir Jahangiri111.37
David Achanccaray202.37
Francisco Sepulveda325226.54