Decoding complex imagery hand gestures

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 01 Jul 2017Embargo end date: 01 Jan 2017Publisher:IEEEJournal:2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Funded by:NSF | CAREER: Signal Models, Ch..., NIH | Optimizing BCI-FIT: Brain..., NSF | CPS: TTP Option: Synergy:... +1 projects

Authors: Seyed Sadegh Mohseni Salehi; Mohammad Moghadamfalahi; Fernando Quivira; Alexander Piers; Hooman Nezamfar; Deniz Erdogmus;

doi: 10.1109/embc.2017.8037480 , 10.48550/arxiv.1703.02929

pmid: 29060521

pmc: PMC6525619

arXiv: 1703.02929

Decoding complex imagery hand gestures

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Abstract

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.

This work has been submitted to EMBC 2017

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Keywords

FOS: Computer and information sciences, Imagery, Psychotherapy, Gestures, Brain-Computer Interfaces, Computer Science - Human-Computer Interaction, Imagination, Humans, Electroencephalography, Hand, Human-Computer Interaction (cs.HC)

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