159 research outputs found

    The hybrid brain-computer interface: a bridge to assistive technology?

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    Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Brain-Computer Interfaces (BCIs) can be extended by other input signals to form a so-called hybrid BCI (hBCI). Such an hBCI allows the processing of several input signals with at least one brain signal for control purposes, i.e. communication and environmental control. This work shows the principle, technology and application of hBCIs and discusses future objectives.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIEC/FP7/288566/EU/Brain-neural computer interfaces on track to home – Development of a practical generation of BNCI for independent home use/BackHom

    Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project

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    In this paper we give an overview of our work on an asynchronous BCI (where the subject makes self-paced decisions on when to switch from a mental task to the next) that responds every 1/2 second. A local neural classifier tries to recognize three different mental tasks, but may also respond unknown for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with different subjects (around 15 up to now). We also describe briefly two brain-actuated applications we have developed, namely a virtual keyboard and a mobile robot (similar to a motorized wheelchair)

    Invasive or Noninvasive: Understanding Brain-Machine Interface Technology

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    Evolution of the Mental States Operating a Brain-Computer Interface

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    This study analyses the location of patterns of brain activity in the signal space while a human subject is trained to operate a brain-computer interface. This evaluation plays an important role in the understanding of the underlying system, and it gives valuable information about the translation algorithms. The relative position and morphology of the patterns in a training session, and from one session to another, enable us to evaluate the performance of both the interface and the user. Thanks to these aforementioned variables we are also able to appreciate stable trajectories of the mental states during the sessions, which shows both the adaptability of the user to the interface, and vice versa

    Non-Invasive Brain-Actuated Control of a Mobile Robot

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    Recent experiments have shown the near possibility to use the brain electrical activity to directly control the movement of robotics or prosthetic devices. In this paper we report results with a portable non-invasive brain-computer interface that makes possible the continuous control of a mobile robot in a house-like environment. The interface uses 8 surface electrodes to measure electroencephalogram (EEG) signals from which a statistical classifier recognizes 3 different mental states. Until now, brain-actuated control of robots has relied on invasive approaches-requiring surgical implantation of electrodes-since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. Here we show that, after a few days of training, two human subjects successfully moved a robot between several rooms by mental control only. Furthermore, mental control was only marginally worse than manual control on the same task

    Non-invasive Brain actuated control of a mobile robot by Human EEG

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    Brain activity recorded non-invasively is sufficient to control a moblie robot if advanced robotics is used in combination with asynchronous EEG analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG based systems have bben considered tto slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only using an EEG based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a preformance ration of 0.74

    3D Trajectory Reconstruction of Upper Limb Based on EEG

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    The main goal of this paper is to simultaneously decode movement velocity of both hand and elbow from electroencephalography (EEG) signals. The result can support motor rehabilitation using a robotic arm and assist people with disabilities to control an upper limb neuroprosthesis in natural movement. In recent works, researchers have estimated hand movement velocity from EEG signals. However, such studies are insufficient to apply motor rehabilitation, since they only considered hand movement trajectory. Sometimes patients take wrong elbow movement in motor rehabilitation even though their hand movements are correct. In this study, we explore to decode not only hand but also elbow velocity from EEG signals when subjects move upper limb

    Asynchronous Non-Invasive Brain-Actuated Control of an Intelligent Wheelchair

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    In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external cue. To do so the users learn to voluntary modulate EEG oscillatory rhythms by executing three mental tasks (i.e., mental imagery) that are associated to different steering commands. Importantly, we implement shared control techniques between the BMI and the intelligent wheelchair to assist the subject in the driving task. The results show that the three subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair
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