A stimulus-independent hybrid BCI based on motor imagery and somatosensory attentional orientation
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Distinctive EEG signals from the motor and so-
matosensory cortex are generated during mental tasks of motor
imagery (MI) and somatosensory attentional orientation (SAO).
In this study, we hypothesize that a combination of these two
signal modalities provides improvements in BCI performance
with respect to using the two methods separately, and generate
novel types of multi-class BCI systems. Thirty-two subjects were
randomly divided into a Control-Group and a Hybrid-Group. In
the Control-Group, the subjects performed left and right hand
motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the
subjects performed the four mental tasks (i.e., L-MI, R-MI, L-
SAO, and R-SAO). The results indicate that combining two of the
tasks in a hybrid manner (such as L-SAO and R-MI), resulted
in a significantly greater classification accuracy than when using
two MI tasks. The hybrid modality reached 86.1% classification
accuracy on average, with a 7.70% increase with respect to MI
(
P <
0
.
01
), and 7.21% to SAO (
P <
0
.
01
) alone. Moreover, all
16 subjects in the hybrid modality reached at least 70% accuracy,
which is considered the threshold for BCI illiteracy. In addition to
the two-class results, the classification accuracy was 68.1% and
54.1% for the 3-class and 4-class hybrid BCI. Combining the
induced brain signals from motor and somatosensory cortex, the
proposed stimulus-independent hybrid BCI has shown improved
performance with respect to individual modalities, reducing the
portion of BCI-illiterate subjects, and provided novel types of
multi-class BCIs.
matosensory cortex are generated during mental tasks of motor
imagery (MI) and somatosensory attentional orientation (SAO).
In this study, we hypothesize that a combination of these two
signal modalities provides improvements in BCI performance
with respect to using the two methods separately, and generate
novel types of multi-class BCI systems. Thirty-two subjects were
randomly divided into a Control-Group and a Hybrid-Group. In
the Control-Group, the subjects performed left and right hand
motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the
subjects performed the four mental tasks (i.e., L-MI, R-MI, L-
SAO, and R-SAO). The results indicate that combining two of the
tasks in a hybrid manner (such as L-SAO and R-MI), resulted
in a significantly greater classification accuracy than when using
two MI tasks. The hybrid modality reached 86.1% classification
accuracy on average, with a 7.70% increase with respect to MI
(
P <
0
.
01
), and 7.21% to SAO (
P <
0
.
01
) alone. Moreover, all
16 subjects in the hybrid modality reached at least 70% accuracy,
which is considered the threshold for BCI illiteracy. In addition to
the two-class results, the classification accuracy was 68.1% and
54.1% for the 3-class and 4-class hybrid BCI. Combining the
induced brain signals from motor and somatosensory cortex, the
proposed stimulus-independent hybrid BCI has shown improved
performance with respect to individual modalities, reducing the
portion of BCI-illiterate subjects, and provided novel types of
multi-class BCIs.
Date Issued
2017-03-17
Date Acceptance
2017-03-07
Citation
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25 (9), pp.1674-1682
ISSN
1558-0210
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
1674
End Page
1682
Journal / Book Title
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume
25
Issue
9
Copyright Statement
© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Subjects
0903 Biomedical Engineering
0906 Electrical And Electronic Engineering
Biomedical Engineering
Publication Status
Published