Abstract
Near-infrared spectroscopy (NIRS) is an optical imaging method that has recently been investigated for non-invasive Brain Computer Interfaces (BCI). The performance of NIRS-based BCI can deteriorate when the number of channels becomes larger. Here we present three types of channel selection methods based on ranked channels, pre-defined channel configurations and statistical analysis for high dimensional NIRS data. The optimal combination of channels is selected by the highest classification accuracy rate based on Linear Discriminant Analysis (LDA). Experimental results show that the three considered types of channel selection methods achieve higher classification performance by removing the noisy and non-informative channels. Also the proposed statistical channel selection method can reduce the computation time significantly without any loss of classification accuracy.
| Original language | English |
|---|---|
| Title of host publication | 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 |
| Pages | 95-97 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - May 17 2013 |
| Externally published | Yes |
| Event | 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of Duration: Feb 18 2013 → Feb 20 2013 |
Publication series
| Name | 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 |
|---|
Other
| Other | 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon Province |
| Period | 2/18/13 → 2/20/13 |
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Keywords
- NIRS-based BCI
- Optimal channel selection
- Statistical channel selection
ASJC Scopus subject areas
- Human-Computer Interaction
Cite this
Optimal channel selection based on statistical analysis in high dimensional NIRS data. / Lee, Min Ho; Fazli, Siamac; Lee, Seong Whan.
2013 International Winter Workshop on Brain-Computer Interface, BCI 2013. 2013. p. 95-97 6506643 (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Optimal channel selection based on statistical analysis in high dimensional NIRS data
AU - Lee, Min Ho
AU - Fazli, Siamac
AU - Lee, Seong Whan
PY - 2013/5/17
Y1 - 2013/5/17
N2 - Near-infrared spectroscopy (NIRS) is an optical imaging method that has recently been investigated for non-invasive Brain Computer Interfaces (BCI). The performance of NIRS-based BCI can deteriorate when the number of channels becomes larger. Here we present three types of channel selection methods based on ranked channels, pre-defined channel configurations and statistical analysis for high dimensional NIRS data. The optimal combination of channels is selected by the highest classification accuracy rate based on Linear Discriminant Analysis (LDA). Experimental results show that the three considered types of channel selection methods achieve higher classification performance by removing the noisy and non-informative channels. Also the proposed statistical channel selection method can reduce the computation time significantly without any loss of classification accuracy.
AB - Near-infrared spectroscopy (NIRS) is an optical imaging method that has recently been investigated for non-invasive Brain Computer Interfaces (BCI). The performance of NIRS-based BCI can deteriorate when the number of channels becomes larger. Here we present three types of channel selection methods based on ranked channels, pre-defined channel configurations and statistical analysis for high dimensional NIRS data. The optimal combination of channels is selected by the highest classification accuracy rate based on Linear Discriminant Analysis (LDA). Experimental results show that the three considered types of channel selection methods achieve higher classification performance by removing the noisy and non-informative channels. Also the proposed statistical channel selection method can reduce the computation time significantly without any loss of classification accuracy.
KW - NIRS-based BCI
KW - Optimal channel selection
KW - Statistical channel selection
UR - http://www.scopus.com/inward/record.url?scp=84877713605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877713605&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2013.6506643
DO - 10.1109/IWW-BCI.2013.6506643
M3 - Conference contribution
SN - 9781467359733
T3 - 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
SP - 95
EP - 97
BT - 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
ER -