Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

• Published in: Frontiers in neuroscience Vol. 16; p. 1009878
• Main Authors: Lee, Hyemin S., Schreiner, Leonhard, Jo, Seong-Hyeon, Sieghartsleitner, Sebastian, Jordan, Michael, Pretl, Harald, Guger, Christoph, Park, Hyung-Soon
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 19.10.2022
• Subjects: BCI; EEG; finger decoding; high-density EEG; machine learning; Neuroscience; ultra-high-density EEG
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.1009878

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10–20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8–12 Hz) and beta (13–25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.