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Decoding brain cognitive activity across subjects using multimodal M/EEG neuroimaging.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2017 Jul; Vol. 2017, pp. 3224-3227. - Publication Year :
- 2017
-
Abstract
- Brain decoding is essential in understanding where and how information is encoded inside the brain. Existing literature has shown that a good classification accuracy is achievable in decoding for single subjects, but multi-subject classification has proven difficult due to the inter-subject variability. In this paper, multi-modal neuroimaging was used to improve two-class multi-subject classification accuracy in a cognitive task of differentiating between a face and a scrambled face. In this transfer learning problem, a feature space based on special-form covariance matrices manipulated with riemannian geometry are used. A supervised two-layer hierarchical model was trained iteratively for estimating classification accuracies. Results are reported on a publically available multi-subject, multi-modal human neuroimaging dataset from MRC Cognition and Brain Sciences Unit, University of Cambridge. The dataset contains simultaneous recordings of electroencephalography (EEG) and magnetoencephalography (MEG). Our model attained, using leave-one-subject-out cross-validation, a classification accuracy of 70.82% for single modal EEG, 81.55% for single modal MEG and 84.98% for multi-modal M/EEG.
Details
- Language :
- English
- ISSN :
- 2694-0604
- Volume :
- 2017
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 29060584
- Full Text :
- https://doi.org/10.1109/EMBC.2017.8037543