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      High density EEG measurment

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      Creators
      Ahmadi, S.
      Date of Archiving
      2019
      Archive
      Radboud Data Repository
      Data archive handle
      https://hdl.handle.net/11633/aacssvje
      Related publications
      Low channel count montages using sensor tying for VEP-based BCI  
      Publication type
      Dataset
      Access level
      Restricted access
      Please use this identifier to cite or link to this item: https://hdl.handle.net/2066/209338   https://hdl.handle.net/2066/209338
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      Organization
      SW OZ DCC AI
      Audience(s)
      Life sciences
      Languages used
      English
      Key words
      EEG; Electrode montage; Layout optimization; BCI; Visual evoked potentials
      Abstract
      Objective: Brain Computer Interfaces (BCIs) are slowly making their appearance on the consumer market, accompanied by a higher popularity among the general public. This new group of users requires easy-to-use headsets with robustness to non-precise placement. In this paper, an optimized fixed montage EEG headset for VEP BCIs is proposed. Approach: The proposed layout covers only the most relevant area with large sensors to account for slight misplacement. To obtain large sensors, without having them physically available, we tie multiple sensors together and simulate the effect by averaging the signal of multiple sensors. Main results: In simulations based on recorded 256-channel EEG data, it is shown that a circular center-surround configuration with sensor tying, leading to only 8 channels covering a large part of the occipital lobe, can provide high performance and good robustness to misplacement. Automatically optimized layouts were unable to achieve better performance, demonstrating the utility of this manual design. Finally, the performance and benefits of sensor tying in the manual design are then validated in a physical experiment. Significance: The resulting proposed layout fulfills most requirements of an easy to use consumer EEG headset.
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      • Datasets [1221]
      • Faculty of Social Sciences [27089]
       
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