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

By Bradley Jay Edelman, Bryan S. Baxter, Bin He. IEEE Transactions on Biomedical Engineering, 2015

340 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/34ea5d044d907d458598ba036c389ab35de17fb9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Social Psychology

Citations in the corpus

Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “While EEG can capture premotor and motor area activation (Edelman et al., 2015; Saha et al., 2019a), intracortical electrodes”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, methodology): “For the sensor domain, the alpha power (8 to 13 Hz) at each electrode was extracted at a 1-Hz resolution using a Morlet wavelet technique previously described in (24).”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “In recent years, some researchers have suggested that source localization of EEG [232] and active data selection [233] can improve classification performance.”
EEG-Based Brain-Computer Interfaces (2017, positive): “Many recent studies have used data-driven spatial ﬁltering methods such as common spatial patterns [38] and source imaging methods [39].”