Material Detail

EEG Feature Representations and Associated Spatial Filters for Brain-Computer Interfaces

EEG Feature Representations and Associated Spatial Filters for Brain-Computer Interfaces

This video was recorded at BBCI Workshop: Advances in Neurotechnologies, Berlin 2012. When designing EEG-based Brain-Computer Interfaces (BCI), a crucial signal processing component is the feature extraction step. It consists in representing EEG signals by a number of values that describe the relevant information they contain. This lecture will first expose the main features that are used to represent EEG signals such as Motor Imagery or P300. However, due to volume conduction, EEG signals inherently have a low spatial resolution, and the information they contain is generally spread over several channels. This makes features extracted individually from each EEG channel not as efficient as it could be. To alleviate this issue and improve the signal-to-noise ratio, it is important to use... Show More
Rate

Quality

  • Editor Reviews
  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.