EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 1; pp. 4 - 14
• Main Authors: Edelman, Bradley J., Baxter, Bryan, He, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Brain mapping; Brain Mapping - methods; Brain-computer interface; Brain-Computer Interfaces; Classification; Decoding; Devices; Disengaging; EEG source imaging; Electrodes; Electroencephalography; Electroencephalography - methods; Female; Hand - physiology; Humans; Imagination - physiology; Imaging; Integrated circuits; Male; Motor imagery; Motors; Neuroimaging; Robot sensing systems; Scalp; Surfaces; Tasks; Three dimensional; Time-frequency analysis; Young Adult; brain mapping; EEG source imaging (ESI); motor imagery (MI); Brain–computer interface (BCI); neuroimaging
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2015.2467312

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 right-hand 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.