A post-stroke rehabilitation system integrating robotics, VR and high-resolution EEG imaging.
IEEE Trans Neural Syst Rehabil Eng. 2013 Sep;21(5):849-59
Authors: Steinisch M, Tana MG, Comani S
We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs. VR applications present all relevant information for task execution as easy-to-understand graphics that do not need any written or verbal instructions. High-resolution electroencephalography (HR-EEG) is synchronized with kinematic data acquisition, allowing for the epoching of EEG signals on the basis of movement-related temporal events. Two healthy volunteers participated in a feasibility study and performed a protocol suggested for the rehabilitation of post-stroke patients. Kinematic data were analyzed by means of in-house code. Open source packages (EEGLAB, SPM, and GMAC) and in-house code were used to process the neurological data. Results from kinematic and EEG data analysis are in line with knowledge from currently available literature and theoretical predictions, and demonstrate the feasibility and potential usefulness of the proposed rehabilitation system to monitor neuro-motor recovery.
Brain-computer interfaces: a powerful tool for scientific inquiry.
Curr Opin Neurobiol. 2014 Apr;25C:70-75
Authors: Wander JD, Rao RP
Abstract. Brain-computer interfaces (BCIs) are devices that record from the nervous system, provide input directly to the nervous system, or do both. Sensory BCIs such as cochlear implants have already had notable clinical success and motor BCIs have shown great promise for helping patients with severe motor deficits. Clinical and engineering outcomes aside, BCIs can also be tremendously powerful tools for scientific inquiry into the workings of the nervous system. They allow researchers to inject and record information at various stages of the system, permitting investigation of the brain in vivo and facilitating the reverse engineering of brain function. Most notably, BCIs are emerging as a novel experimental tool for investigating the tremendous adaptive capacity of the nervous system.
Android Wear will show you info from the wide variety of Android apps, such as messages, social apps, chats, notifications, health and fitness, music playlists, and videos.
It will also enable Google Now functions — say “OK, Google” for flight times, sending a text, weather, view email, get directions, travel time, making a reservation, etc..
Google says it’s working with several other consumer-electronics manufacturers, including Asus, HTC, and Samsung; chip makers Broadcom, Imagination, Intel, Mediatek and Qualcomm; and fashion brands like the Fossil Group to offer watches powered by Android Wear later this year.
If you’re a developer, there’s a new section on developer.android.com/wear focused on wearables. Starting today, you can download a Developer Preview so you can tailor your existing app notifications for watches powered by Android Wear.
A Hybrid Brain Computer Interface System Based on the Neurophysiological Protocol and Brain-actuated Switch for Wheelchair Control.
J Neurosci Methods. 2014 Apr 5;
Authors: Cao L, Li J, Ji H, Jiang C
BACKGROUND: Brain Computer Interfaces (BCIs) are developed to translate brain waves into machine instructions for external devices control. Recently, hybrid BCI systems are proposed for the multi-degree control of a real wheelchair to improve the systematical efficiency of traditional BCIs. However, it is difficult for existing hybrid BCIs to implement the multi-dimensional control in one command cycle.
NEW METHOD: This paper proposes a novel hybrid BCI system that combines motor imagery (MI)-based bio-signals and steady-state visual evoked potentials (SSVEPs) to control the speed and direction of a real wheelchair synchronously. Furthermore, a hybrid modalities-based switch is firstly designed to turn on/off the control system of the wheelchair.
RESULTS: Two experiments were performed to assess the proposed BCI system. One was implemented for training and the other one conducted a wheelchair control task in the real environment. All subjects completed these tasks successfully and no collisions occurred in the real wheelchair control experiment.
COMPARISON WITH EXISTING METHOD(S): The protocol of our BCI gave much more control commands than those of previous MI and SSVEP-based BCIs. Comparing with other BCI wheelchair systems, the superiority reflected by the index of path length optimality ratio validated the high efficiency of our control strategy.
CONCLUSIONS: The results validated the efficiency of our hybrid BCI system to control the direction and speed of a real wheelchair as well as the reliability of hybrid signals-based switch control.
Glyph looks like a normal headset and operates like one, too. That is, until you move the headband down over your eyes and it becomes a fully-functional visual visor that displays movies, television shows, video games or any other media connected via the attached HDMI cable.
Using Virtual Retinal Display (VRD), a technology that mimics the way we see light, the Glyph projects images directly onto your retina using one million micromirrors in each eye piece. These micromirrors reflect the images back to the retina, producing a reportedly crisp and vivid quality.