EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three...

Full description

Saved in:
Bibliographic Details
Published in:Journal of neural engineering 2012-04, Vol.9 (2), p.026014-1-11
Main Authors: Mak, Joseph N, McFarland, Dennis J, Vaughan, Theresa M, McCane, Lynn M, Tsui, Phillippa Z, Zeitlin, Debra J, Sellers, Eric W, Wolpaw, Jonathan R
Format: Article
Language:English
Subjects:
Aged
Algorithms
ALS
Amplitudes
Amyotrophic Lateral Sclerosis - psychology
BCI
Brain - physiology
Brain Mapping
Correlation
Data Interpretation, Statistical
Disabled Persons
Discriminant Analysis
EEG
Electroencephalography
ERP
Event-Related Potentials, P300 - physiology
Female
Forecasting
Human-computer interface
Humans
Least-Squares Analysis
Linear Models
Male
Mathematical models
Middle Aged
On-line systems
Online Systems
Optimization
P300
Performance enhancement
Photic Stimulation
predictors
Reproducibility of Results
Reproduction
Software
Theta Rhythm - physiology
User-Computer Interface
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.
ISSN:1741-2560
1741-2552
DOI:10.1088/1741-2560/9/2/026014