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.

This electroencephalography (EEG) study provides significant neurophysiological evidence for the processing of words outside conscious awareness. Brain potentials were recorded while 25 participants were presented with words and shapes, each of them with 17, 34, and 67ms presentation duration times ("no presentation" was included as control condition). Participants were instructed to report whether they have seen anything and if yes, what it was (shape or word). If they saw a word and were able to read it, they were asked to report having read it. Crucially, even though only 2.9% of the 17ms word presentations were classified as readable, these presentations elicited significant brain activity near the Wernicke area that was missing in the case of 17ms shape presentations. The respective brain activity difference lasted for about 150ms being statistically significant between 349ms and 409ms after stimulus onset. It is suggested that this neurophysiological difference reflects nonconscious (i.e., subliminal) word processing. Some aspects of the NeuroIS discipline include text messages and the current findings demonstrate that those text messages can be processed by the nervous system even in the absence of their conscious recognition.

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