![]() 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)%. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. A linear support vector machine (SVM) was used for pairwise finger classification. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. This study exploits mu (8–12 Hz) and beta (13–25 Hz) band power features for classification and topography plots. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. 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. 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. ![]() ![]() In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). 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). 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. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement.
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