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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kai Keng Ang Juanhong Yu Cuntai Guan |
| Copyright Year | 2012 |
| Description | Author affiliation: Institute for Infocomm Research - Agency for Science and Technology And Research, Singapore (Kai Keng Ang; Juanhong Yu; Cuntai Guan) |
| Abstract | Near-Infrared Spectroscopy (NIRS)-based Brain-Computer Interface (BCI) was recently studied for numerical cognition. This study presents a study using high density 348 channels NIRS-based BCI from 8 healthy subjects while solving mental arithmetic problems with two difficulty levels and the rest condition. The existing feature extraction and selection methods on the existing study were presented only for low density 16 channels NIRS-based BCI, and required the specification on the number of features to select to yield desirable performance. This paper presents a method of extracting discriminative features from high density single-trial NIRS data using common average reference spatial filtering and single-trial baseline reference, and a method of automatically selecting a set of discriminative and non-redundant features using the Mutual Information-based Rough Set Reduction (MIRSR) and Supervised Pseudo Self- Evolving Cerebellar (SPSEC) algorithms. The performance of the proposed method is evaluated using 5×5-fold cross-validations on the single-trial NIRS data collected using the support vector machine classifier. The results yielded an overall average accuracy of 71.4% and 91.0% in classifying hard versus easy tasks and hard versus rest tasks respectively using the proposed method, compared to 46.1% and 62.2% respectively using existing methods. The results demonstrated the effectiveness of using the proposed feature extraction and selection method in high density NIRS-based BCI for assessing numerical cognition. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1390392 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252604 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Classification algorithms Accuracy Optical filters Adaptive optics Iron Cognition feature selection Brain-Computer interface near-infrared spectroscopy mental arithmetic feature extraction |
| Content Type | Text |
| Resource Type | Article |
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