Abstract
Brain-Computer Interfaces (BCIs) enable its users to interact with their environment only by thought. Earlier studies indicated [1, 2] that
BCI might be a suitable method for controlling a neuroprostheses, which could assist people with spinal cord injuries (SCI) in their daily life. One
drawback for the end user is that only simple motor imaginations (MI) are available for control e.g. MI of both feet to control ones arm is abstract
and in contradiction to an associated natural movement. Therefore we are looking for means to design a more natural control modality. One
promising scenario would be to use MI of different grasps to actually control different grasps of the neuroprosthesis. In this study we
attempt to classify the execution of different grasp types in low-frequency time-domain EEG signals.
BCI might be a suitable method for controlling a neuroprostheses, which could assist people with spinal cord injuries (SCI) in their daily life. One
drawback for the end user is that only simple motor imaginations (MI) are available for control e.g. MI of both feet to control ones arm is abstract
and in contradiction to an associated natural movement. Therefore we are looking for means to design a more natural control modality. One
promising scenario would be to use MI of different grasps to actually control different grasps of the neuroprosthesis. In this study we
attempt to classify the execution of different grasp types in low-frequency time-domain EEG signals.
Original language | English |
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Publication status | Published - 1 Jun 2016 |
Event | 6th International BCI Meeting - Pacific Grove, CA, Asilomar, United States Duration: 30 May 2016 → 3 Jun 2016 |
Conference
Conference | 6th International BCI Meeting |
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Country/Territory | United States |
City | Asilomar |
Period | 30/05/16 → 3/06/16 |