Abstract:
OBJECTIVE:The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. APPROACH:Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. MAIN RESULTS:VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). SIGNIFICANCE:These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
C Schudlo L,Chau Tdoi
10.1088/1741-2560/12/6/066008subject
Has Abstractpub_date
2015-12-01 00:00:00pages
066008issue
6eissn
1741-2560issn
1741-2552journal_volume
12pub_type
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journal_title:Journal of neural engineering
pub_type: 杂志文章
doi:10.1088/1741-2560/8/4/046029
更新日期:2011-08-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
doi:10.1088/1741-2560/3/2/008
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journal_title:Journal of neural engineering
pub_type: 临床试验,杂志文章
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pub_type: 杂志文章,评审
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更新日期:2009-02-01 00:00:00
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pub_type: 杂志文章
doi:10.1088/1741-2560/6/4/046009
更新日期:2009-08-01 00:00:00
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更新日期:2011-06-01 00:00:00
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