Name
Abstract | ||
|---|---|---|
Passive brain-computer interface (BCI) can monitor cognitive function through physiological signals in human-machine system. This paper established a passive BCI based on functional near-infrared spectroscopy (fNIRS) to detect the sustained attentional load. Three levels of attentional load were adjusted by modifying the number of stimulate in feature-absence Continuous Performance Test (CPT) tasks. 15 healthy subjects were recruited in total, and 10 channels were measured in prefrontal cortex (PFC). Performance and NASA-TLX scales were also recorded as reference. The mean value of oxyhemoglobin and deoxyhemoglobin, signal slope, power spectrum and approximate entropy in 0–10 s were extracted from raw fNIRS signal for support vector machine (SVM) classification. The best performance features were selected by SVM-RFE algorithm. In conclusion over 80% average accuracy was achived between easy and hard attentional load, which demonstrated fNIRS can be a proposed method to detect sustained attention load for a passive BCI. |
Year | Venue | Field |
|---|---|---|
2016 | BICS | Approximate entropy,Pattern recognition,Computer science,Brain–computer interface,Prefrontal cortex,Support vector machine,Spectral density,Artificial intelligence,Constant false alarm rate,Cognition,Motor imagery |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Zhang | 1 | 97 | 19.98 |
| Xuejun Jiao | 2 | 0 | 2.03 |
| Jin Jiang | 3 | 2 | 2.06 |
| Jinjin Pan | 4 | 0 | 0.34 |
| Yong Cao | 5 | 0 | 0.68 |
| Hanjun Yang | 6 | 0 | 0.34 |
| Fenggang Xu | 7 | 0 | 0.34 |