Motor imagery decoding in the source domain based on dipole imaging and three-dimensional convolutional neural network
Objective To fully preserve and utilize the spatiotemporal information of dipoles during motor imagery (MI),this paper proposes a novel dipole imaging (DI) method combined with a 3D convolutional neural network (3DCNN) for source-domain MI decoding (DI-3DCNN).Methods Firstly,based on the electroencephalography source imaging (ESI) technique,dipole source estimation of MI-EEG is computed.Secondly,the average dipole source estimation for each MI task is obtained.By automatically selecting the time points with high dipole activation levels and maximum discrimination from other tasks,the moments are chosen as central sampling points.These central points are extended forwards and backwards in time and combined in task order to form time of interest (TOI) .Subsequently,the Desikan-Killiany (DK) neuroanatomical partitions covering highly activated dipoles is chosen,and a local preserving projection method (LPP) with DK atlas constraint (LPPDK) is employed.Then,dipole coordinates within the selected left and right brain partitions are separately reduced from 3D to 2D using LPPDK,obtaining dipole 2D coordinates with neurophysiological prior information.These 2D coordinates are combined with dipole amplitude information at each sampling point within TOI for imaging.Interpolation and downsampling are performed to obtain 2D amplitude maps of dipoles.After that,2D amplitude maps of dipoles within TOI are stacked in chronological order to obtain 3D dipole feature maps of left and right brains,which serve as the input data for the network.At last,a dual-branch 3D convolutional neural network (DB3DCNN) is designed to decode MI based on the characteristics of input data.Results Experimental studies conducted on the BCI competition Ⅳ 2a dataset demonstrate an average decoding accuracy of 86.50％.Conclusions The 3D dipole feature maps obtained through DI effectively preserve the optimal activation time,intensity,and physiological spatial information of dipoles,and are suitable for DB3DCNN.
motor imageryEEG source imaginglocal preserving projectionconvolutional neural networkDesikan-Killiany partition
万方数据
维普
