Doubling the Speed of N200 Speller via Dual-Directional Motion Encoding

Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention

A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.

Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022

The National Natural Science Foundation of China is one of the major funding agencies for neurorehabilitation research in China. This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and worldwide. We used data from the Web of Science Core Collection (WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information. In addition, the prospects for neurorehabilitation research in China are discussed. From 2010 to 2022, a total of 74,220 publications in neurorehabilitation were identified, with there being an overall upward tendency. During this period, the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neurorehabilitation research in China. With the support of the National Natural Science Foundation of China, China has made some achievements in neurorehabilitation research. Research related to neurorehabilitation is believed to be making steady and significant progress in China.

Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention

Paralysis affects roughly 1 in 50 Americans. While there is no cure for the condition, brain-computer interfaces (BCI) can allow users to control a device with their mind, bypassing the paralyzed region. Non-invasive BCIs still have high error rates, which is hypothesized to be reduced with concurrent targeted neuromodulation. This study examines whether transcranial focused ultrasound (tFUS) modulation can improve BCI outcomes, and what the underlying mechanism of action might be through high-density electroencephalography (EEG)-based source imaging (ESI) analyses. V5-targeted tFUS significantly reduced the error for the BCI speller task. ESI analyses showed significantly increased theta activity in the tFUS condition at both V5 and downstream the dorsal visual processing pathway. Correlation analysis indicates that the dorsal processing pathway connection was preserved during tFUS stimulation, whereas extraneous connections were severed. These results suggest that V5-targeted tFUS' mechanism of action is to raise the brain's feature-based attention to visual motion.

A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN

Objective

Brain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.

Approach

In this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.

Main results

The performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).

Significance

The proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

Intracranial brain-computer interface spelling using localized visual motion response

Intracranial brain-computer interfaces (BCIs) can assist severely disabled persons in text communication and environmental control with high precision and speed. Nevertheless, sustainable BCI implants require minimal invasiveness. One of the implantation strategies is to adopt localized and robust cortical activities to drive BCI communication and to make a precise presurgical planning. The visual motion response is a good candidate for inclusion in this strategy because of its focal activity over the middle temporal visual area (MT). Here, we developed an intracranial BCI for spelling, utilizing only three electrodes over the MT area. The best recording electrodes were decided by preoperative functional magnetic resonance imaging (MRI) localization of the MT, and local neural activities were further enhanced by differential rereferencing of these electrodes. The BCI spelling system was validated both offline and online by five epilepsy patients, achieving the fastest speed of 62 bits/min, i.e., 12 characters/min. Moreover, the response patterns of dual-directional visual motion stimuli provided an additional dimension of BCI target encoding and paved the way for a higher information transfer rate of intracranial BCI spelling.

Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes

Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be continuously released to the scalp-electrode interface during used. The electrolyte solution can infiltrate the stratum corneum and reduce the scalp-electrode impedance to 10 kΩ-15 kΩ. The flexible structure enables the electrode with mechanical stability, increases the wearing comfort, and reduces the scalp-electrode gap to reduce contact impedance. As a result, a long-term BCI application based on measurements of motion-onset visual evoked potentials (mVEPs) shows that the 3-hour BCI accuracy of the new electrode (77% to 100%) is approximately the same as that of conventional electrodes supported by a conductive gel during the first hour. Furthermore, the BCI system based on the new electrode can retain low contact impedance for 10 hours on scalp, which greatly improved the ability of BCI technique.