RETRACTED ARTICLE: Multimedia remote interactive operations based on EEG signals constructed BCI with convolutional neural network
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Brain-computer interfaces (BCIs) were useful in many scenarios; however current computer screen-based BCIs (CS-BCIs) were not wearable. We proposed AR-BCI, a BCI combined with augmented reality, in which a translucent head-mounted display (HMD) was used as the user interface so that the users could wear this AR-BCI and see virtual stimuli and real environment simultaneously. We recruited ten subjects in this study. Each subject completed predefined tasks with the AR-BCI (as the experiment group) and a CS-BCI (as the control group) respectively. We used the accuracies and information transfer rates (ITRs) of the BCIs as the performance metrics. The experimental results showed that two BCIs had almost the same accuracies (about 81% for the AR-BCI and about 82% for the CS-BCI) and almost the same ITRs (about 11.3 bits/min for the AR-BCI and about 11.7 bits/min for the CS-BCI). The AR-BCI we proposed might be useful for a disability assistance scenario.
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Motor Imagery
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All brain–computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.
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Sensorimotor rhythm
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In this paper we introduce the concept of Brain-Computer Interface (BCI) inhibitor, which is meant to standby the BCI until the user is ready, in order to improve the overall performance and usability of the system. BCI inhibitor can be defined as a system that monitors user's state and inhibits BCI interaction until specific requirements (e.g. brain activity pattern, user attention level) are met. In this pilot study, a hybrid BCI is designed and composed of a classic synchronous BCI system based on motor imagery and a BCI inhibitor. The BCI inhibitor initiates the control period of the BCI when requirements in terms of brain activity are reached (i.e. stability in the beta band). Preliminary results with four participants suggest that BCI inhibitor system can improve BCI performance.
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In the last decade, numerous brain-computer interface (BCI) studies have been performed to help disabled people. However, previous BCI methods have several limitations. First, the BCI system has a time delay which makes user inconvenience. Second, user of the BCI only can select a choice among limited options. Last, accuracy of the BCI is low which is a barrier to practical use. Here, we suggested a novel BCI method to solve these problems. Our results demonstrated that BCI response time can be reduced by predicting motor intentions from the readiness potential (Bereitschaftspotential; RP or BP) signals. We also showed that movement trajectory could be predicted from the non-invasive MEG signals with considerably high accuracy. In addition, our results revealed that the BCI performance will be improved by combining feedback information. Furthermore, we showed possibility of a BCI with tactile feedback. Our results will promote the development of a practical BCI system.
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Information Transfer
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Brain Computer Interface(BCI) 기술은 뇌에서 발생한 신호를 직접 해석하여 신체 다른 기관의 표현없이 대상의 의도를 파악하는 기술을 말한다. 실제 BCI응용의 경우 시각, 청각, 촉각 등 다양한 방법으로 단서(cue)를 제시하고 이를 기반으로 동작 할 수 있는데, 현재 많은 연구들은 BCI알고리즘의 훈련데이터와 평가데이터 사이에서 같은 종류의 단서만을 사용하여 연구를 진행하였다. 본 연구에서는 비침습형 BCI의 대표적인 방식인 EEG 기반 BCI 응용을 시각단서와 청각단서를 이용하여 평가해보았다. 본 연구의 목적은 서로 다른 종류의 단서를 사용하더라도 같은 운동심상(Motor imagery)의 뜻을 갖는다면 같은 종류의 단서를 사용한 것과 같은 BCI 성능을 나타낼 수 있는지를 평가하는 것에 있다. 평가의 대상이 되는 BCI 알고리즘은 Common Spatial Pattern(CSP)과 Support Vector Machine(SVM)을 기반으로 왼쪽 또는 오른쪽 팔을 움직이는 운동심상을 분류하며, 임상평가를 통해 실험(30명, 23.8 2.78세)을 진행하였다. 성능 평가 결과 다른 종류의 단서를 사용하더라도 같은 운동심상을 수행하고 있다면 같은 종류의 단서를 사용한 것과 유사한 BCI 특징을 얻을 수 있었다.
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The P300-based brain-computer interface (P300 BCI) is currently a very popular topic in assistive technology development. However, only a few simple P300 BCI-based games have been designed so far. Here, we analyze the shortcomings of this BCI in gaming applications and show that solutions for overcoming them already exist, although these techniques are dispersed over several different games. Additionally, new approaches to improve the P300 BCI accuracy and flexibility are currently being proposed in the more general P300 BCI research. The P300 BCI, even in its current form, not only exhibits relatively high speed and accuracy, but also can be used without user training, after a short calibration. Taking these facts together, the broader use of the P300 BCI in BCI-controlled video games is recommended.
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