EEG source imaging enhances the decoding of complex right-hand motor imagery tasks

Bradley J. Edelman, Bryan Baxter, Bin He

Research output: Contribution to journalArticlepeer-review

233 Scopus citations


Goal: Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. Methods: We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation. Results: We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method. Conclusion: ESI is able to enhance BCI performance of decoding complex righth- and motor imagery tasks. Significance: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

Original languageEnglish (US)
Article number2467312
Pages (from-to)4-14
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number1
StatePublished - Jan 2016

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Brain mapping
  • Brain-computer interface (BCI)
  • EEG source imaging (ESI)
  • Motor imagery (MI)
  • Neuroimaging


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