Improving Movement-Related Cortical Potential Detection at the EEG Source Domain

Decoding movement-related cortical potential (MRCP) plays an important role in the Brain-computer interface (BCI) system. MRCP is not easy to be detected on sensors due to the volume conduction effect. This work combines the scout EEG source imaging (ESI) and Locality Preserving Projection (LPP) followed by a linear discriminant analysis (LDA) classifier to detect MRCP of motor imagery and execution. Seven healthy subjects participated in this study and performed cue-based ballistic dorsiflexion. Our results showed that the source domain-based method achieved a significantly higher true positive rate (TPR) than that obtained from the sensor domain in both motor imagery (MI) (76.65±4.26% vs. 70.3±5.4%) and motor execution (ME) (81.66±2.55% vs. 74.49±6.48%) tasks. The false-positive rate (FPR) calculated in the source or sensor space for MI and ME was (24.46±5.07% vs. 28.59±5.17%) and (22.52±3.35% vs. 25.99±7.37%), respectively. Therefore, we demonstrated that EEG signals obtained from the source domain could improve the MRCP detection rather than those in the sensor domain.

[1]  Mads Jochumsen,et al.  Detection and classification of movement-related cortical potentials associated with task force and speed , 2013, Journal of neural engineering.

[2]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[3]  Mads Jochumsen,et al.  A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials , 2015, Comput. Math. Methods Medicine.

[4]  N. Birbaumer,et al.  Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.

[5]  Ning Jiang,et al.  Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications , 2014, IEEE Transactions on Biomedical Engineering.

[6]  Bin He,et al.  Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics. , 2018, Annual review of biomedical engineering.

[7]  V. Brümmer,et al.  Changes in brain cortical activity measured by EEG are related to individual exercise preferences , 2009, Physiology & Behavior.

[8]  Yimin Hou,et al.  A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN , 2020, Journal of neural engineering.

[9]  Takashi Hanakawa,et al.  Generators of Movement-Related Cortical Potentials: fMRI-Constrained EEG Dipole Source Analysis , 2002, NeuroImage.

[10]  C. Tsai,et al.  Movement related cortical potentials of cued versus self‐initiated movements: Double dissociated modulation by dorsal premotor cortex versus supplementary motor area rTMS , 2012, Human brain mapping.

[11]  Christopher C. Cline,et al.  Noninvasive neuroimaging enhances continuous neural tracking for robotic device control , 2019, Science Robotics.

[12]  J. Millán,et al.  Detection of self-paced reaching movement intention from EEG signals , 2012, Front. Neuroeng..

[13]  Songmin Jia,et al.  Decoding of motor imagery EEG based on brain source estimation , 2019, Neurocomputing.

[14]  Bin He,et al.  Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy , 2007, Journal of neural engineering.

[15]  Motoaki Kawanabe,et al.  Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG , 2009, IEEE Transactions on Biomedical Engineering.

[16]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[17]  A. Pavlovic,et al.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. , 2016, Journal of neurophysiology.

[18]  D. Farina,et al.  The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity , 2017, Brain Research.

[19]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[20]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.