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.
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.
Authors: Jianjun Meng; John Mundahl; Taylor Streitz; Kaitlin Maile; Nicholas Gulachek; Jeffrey He; Bin He Journal: IEEE Access Date: 2017-09-11 Impact factor: 3.367
Authors: Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2018-05 Impact factor: 3.802
Authors: James R Stieger; Stephen Engel; Haiteng Jiang; Christopher C Cline; Mary Jo Kreitzer; Bin He Journal: Cereb Cortex Date: 2021-01-01 Impact factor: 5.357