Abstract
Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous, which means, they rely on cues or tasks to which a subject has to react. It would be more useful for users if they could control a device at their own will (i.e., asynchronous BCIs). However, previous asynchronous BCI systems that rely on non-invasive electroencephalogram (EEG) measurements, are not accurate and stable enough for real world applications. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of synchronous motor imagery (MI) tasks. In this study, we present a first report on an asynchronous multi-modal hybrid BCI, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements and propose novel subject-dependent classification strategies for combining both measurements.
| Original language | English |
|---|---|
| Title of host publication | 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 |
| Publisher | IEEE Computer Society |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of Duration: 2014 Feb 17 → 2014 Feb 19 |
Other
| Other | 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon |
| Period | 14/2/17 → 14/2/19 |
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Keywords
- Asynchronous BCI
- Combined EEG-NIRS
- Hybrid Brain-Computer Interface
- Subject-dependent Classification
ASJC Scopus subject areas
- Human-Computer Interaction
- Human Factors and Ergonomics
Cite this
Hybrid brain-computer interface based on EEG and NIRS modalities. / Lee, Min Ho; Fazli, Siamac; Mehnert, Jan; Lee, Seong Whan.
2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014. 6782577.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hybrid brain-computer interface based on EEG and NIRS modalities
AU - Lee, Min Ho
AU - Fazli, Siamac
AU - Mehnert, Jan
AU - Lee, Seong Whan
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous, which means, they rely on cues or tasks to which a subject has to react. It would be more useful for users if they could control a device at their own will (i.e., asynchronous BCIs). However, previous asynchronous BCI systems that rely on non-invasive electroencephalogram (EEG) measurements, are not accurate and stable enough for real world applications. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of synchronous motor imagery (MI) tasks. In this study, we present a first report on an asynchronous multi-modal hybrid BCI, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements and propose novel subject-dependent classification strategies for combining both measurements.
AB - Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous, which means, they rely on cues or tasks to which a subject has to react. It would be more useful for users if they could control a device at their own will (i.e., asynchronous BCIs). However, previous asynchronous BCI systems that rely on non-invasive electroencephalogram (EEG) measurements, are not accurate and stable enough for real world applications. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of synchronous motor imagery (MI) tasks. In this study, we present a first report on an asynchronous multi-modal hybrid BCI, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements and propose novel subject-dependent classification strategies for combining both measurements.
KW - Asynchronous BCI
KW - Combined EEG-NIRS
KW - Hybrid Brain-Computer Interface
KW - Subject-dependent Classification
UR - http://www.scopus.com/inward/record.url?scp=84899419460&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899419460&partnerID=8YFLogxK
U2 - 10.1109/iww-BCI.2014.6782577
DO - 10.1109/iww-BCI.2014.6782577
M3 - Conference contribution
AN - SCOPUS:84899419460
BT - 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
PB - IEEE Computer Society
ER -