| Autor: |
Mohseni Salehi SS, Moghadamfalahi M, Quivira F, Piers A, Nezamfar H, Erdogmus D |
| Jazyk: |
angličtina |
| Zdroj: |
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. 2968-2971. |
| DOI: |
10.1109/EMBC.2017.8037480 |
| Abstrakt: |
Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy-intended grasp prediction probability-of 64.5% for 8 different hand gestures, more than 5 times the chance level. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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