Objective Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. Methods We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. Results The average online prediction time was 0.62 ± 0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. Conclusion Human voluntary movement can be predicted before movement occurs. Significance The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.

Objective Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. Methods Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10 minutes and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5 – 2 hours in a single visit. Results Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1±8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4±3.4% with motor execution and 83.3±8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery. Conclusion Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior. Significance The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients.

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Herein, we report the preparation of poly (cyclotriphosphazene-co-4,4'-sulfonyldiphenol) (PZS) nanotubes decorated with Ag nanoparticles (NPs). The PZS nanotubes have been synthesized firstly via an in situ template approach, and then Ag nanoparticles were prepared via in situ reduction of AgNO3 with NaBH4 as the reductant, supported on the surface of the PZS nanotubes. The as-obtained PZS nanotubes and PZS@Ag NPs composites were characterized by means of Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM) equipped with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), atomic absorption spectroscopy (AAS), thermogravimetric analysis (TGA), and UV-vis spectroscopy. Characterization results showed that Ag NPs with good dispersibility were well anchored onto the surface of the PZS nanotubes with superior thermal stability. In addition, the catalytic activities and reusability of these composites were investigated by employing the reduction of 4-nitrophenol (4-NP) into 4-aminophenol (4-AP) by NaBH4 as a model reaction.