Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/625453
Title: EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks
Authors: Bradley J. Edelman;Bryan Baxter;Bin He
subject: Brain-computer interface|Motor imagery|Brain mapping|EEG source imaging|Neuroimaging
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/170037
http://localhost/handle/Hannan/625453
ISSN: 0018-9294
1558-2531
volume: 63
issue: 1
Appears in Collections:2016

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Title: EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks
Authors: Bradley J. Edelman;Bryan Baxter;Bin He
subject: Brain-computer interface|Motor imagery|Brain mapping|EEG source imaging|Neuroimaging
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/170037
http://localhost/handle/Hannan/625453
ISSN: 0018-9294
1558-2531
volume: 63
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7192613.pdf796.28 kBAdobe PDFThumbnail
Preview File
Title: EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks
Authors: Bradley J. Edelman;Bryan Baxter;Bin He
subject: Brain-computer interface|Motor imagery|Brain mapping|EEG source imaging|Neuroimaging
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/170037
http://localhost/handle/Hannan/625453
ISSN: 0018-9294
1558-2531
volume: 63
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7192613.pdf796.28 kBAdobe PDFThumbnail
Preview File