Decoding Arm Movement Direction Using Ultra-High-Density EEG

Detecting arm movement direction is significant for individuals with upper-limb motor disabilities to restore independent self-care abilities. It involves accurately decoding the fine movement patterns of the arm, which has become feasible using invasive brain-computer interfaces (BCIs). However, it is still a significant challenge for traditional electroencephalography (EEG) based BCIs to decode multi-directional arm movements effectively. This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in different directions. To extract discriminative features from UHD EEG, we proposed a spatial filtering method combining principal component analysis (PCA) and discriminative spatial pattern (DSP). We collected EEG signals from five healthy subjects (two left-handed and three right-handed) to verify the system's feasibility. The movement-related cortical potentials (MRCPs) showed a certain degree of separability both in waveforms and spatial patterns for arm movements in different directions. This study achieved an average classification accuracy of 63. 15 (8. 71)% for both arms (eight-class task) with a peak accuracy of 77. 24%. For the dominant arm (four-class task), we obtained an average accuracy of 75. 31 (9. 21)% with a peak accuracy of 85. 00%. For the first time, this study simultaneously decodes multi-directional movements of both arms using UHD EEG. This study provides a promising approach for detecting information about arm movement directions, which is significant for the development of BCIs.

Authors

• Zhen Ma
• Xinyi Yang
• Jiayuan Meng
• Kun Wang PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, China
• Minpeng Xu
• Dong Ming

Keywords

• Electroencephalography
• Principal component analysis
• Decoding
• Accuracy
• Bioinformatics
• Support vector machines
• Spatial resolution
• Power harmonic filters
• Motors
• Hands
• Arm Movements
• Direction Of Arm Movement
• Electrode
• Healthy Subjects
• Spatial Patterns
• Classification Accuracy
• Average Accuracy
• EEG Signals
• Spatial Filter
• Average Classification Accuracy
• EEG System
• Dominant Arm
• Peak Accuracy
• Feasibility Of The System
• Time Window
• Support Vector Machine
• Frequency Band
• Classification Performance
• Spinal Cord Injury
• Left Arm
• Left Channels
• Event-related Spectral Perturbation
• Higher Average Accuracy
• Common Spatial Pattern
• Results In Fig
• Scikit-learn Python Package
• Entire Band
• Movement Intention
• EEG Data
• Brain-computer Interface (BCI)
• ultra-high-density electroencephalography (UHD EEG)
• arm movement direction
• movement-related cortical potential (MRCP)
• Humans
• Arm
• Movement
• Signal Processing, Computer-Assisted
• Male
• Adult
• Female
• Brain-Computer Interfaces
• Young Adult
• Algorithms