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| Content Provider | PubMed Central |
|---|---|
| Author | Agashe, Harshavardhan A. Contreras-vidal, Jose L. |
| Abstract | Shared control is emerging as a likely strategy for controlling neuroprosthetic devices, in which users specify high level goals but the low-level implementation is carried out by the machine. In this context, predicting the discrete goal is necessary. Although grasping various objects is critical in determining independence in daily life of amputees, decoding of different grasp types from noninvasively recorded brain activity has not been investigated. Here we show results suggesting electroencephalography (EEG) is a feasible modality to extract information on grasp types from the user’s brain activity. We found that the information about the intended grasp increases over the grasping movement, and is significantly greater than chance up to 200 ms before movement onset. |
| Related Links | http://dx.doi.org/10.1109/embc.2013.6610817 |
| Starting Page | 5590 |
| File Format | |
| ISSN | 1557170X |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| Volume Number | 2013 |
| Language | English |
| Publisher Date | 2013-07-01 |
| Access Restriction | Open |
| Subject Keyword | Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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