Abstract
Recent progress in the number of studies involving
brain connectivity analysis of motor imagery (MI) tasks for
brain-computer interface (BCI) systems has warranted the
need for pre-processing methods. The objective of this study
is to evaluate the impact of current source density (CSD)
estimation from raw electroencephalogram (EEG) signals on
the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial
Granger causality (PGC) method was implemented on the raw
EEG signals and CSD signals of a publicly available dataset
for the estimation of brain connectivity features. Moreover,
pairwise binary classifications of four different MI tasks were
performed in inter-session and intra-session conditions using a
support vector machine classifier. The results showed that CSD
provided a statistically significant increase of the AUC: 20.28%
in the inter-session condition; 12.54% and 13.92% with session
01 and session 02, respectively, in the intra-session condition.
These results show that pre-processing of EEG signals is crucial
for single-trial connectivity features based MI-BCI systems and
CSD can enhance their overall performance.
brain connectivity analysis of motor imagery (MI) tasks for
brain-computer interface (BCI) systems has warranted the
need for pre-processing methods. The objective of this study
is to evaluate the impact of current source density (CSD)
estimation from raw electroencephalogram (EEG) signals on
the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial
Granger causality (PGC) method was implemented on the raw
EEG signals and CSD signals of a publicly available dataset
for the estimation of brain connectivity features. Moreover,
pairwise binary classifications of four different MI tasks were
performed in inter-session and intra-session conditions using a
support vector machine classifier. The results showed that CSD
provided a statistically significant increase of the AUC: 20.28%
in the inter-session condition; 12.54% and 13.92% with session
01 and session 02, respectively, in the intra-session condition.
These results show that pre-processing of EEG signals is crucial
for single-trial connectivity features based MI-BCI systems and
CSD can enhance their overall performance.
Original language | English |
---|---|
Title of host publication | Proc. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Number of pages | 4 |
ISBN (Electronic) | 978-1-5386-3646-6 |
Publication status | Published (in print/issue) - 29 Oct 2018 |
Event | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, United States Duration: 18 Jul 2019 → 21 Jul 2019 http://10.1109/EMBC.2018.8513417 |
Conference
Conference | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
---|---|
Country/Territory | United States |
City | Honolulu, HI |
Period | 18/07/19 → 21/07/19 |
Internet address |
Keywords
- Current source density
- scalp-level brain connectivity features
- motor-imagery