Measuring the orientation of segmented Deep Brain Stimulation leads using electroencephalography
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Abstract Segmented Deep Brain Stimulation (DBS) leads enable current steering in specific directions, but this comes with an increased level of programming complexity. Precise measurement of lead orientation is crucial for facilitating stimulation programming. Presently employed methods involve radiation technology posing inherent risks and limitations. Additionally, the potential rotation of leads post-implantation may require repeated measurements. To address these challenges, we propose an outpatient-friendly, radiation-free method using post-operative imaging-informed electroencephalography (EEG). The method was tested in an EEG phantom yielding maximal errors of under 10 ° with under 30s of data. It works with as few as 4 EEG electrodes with only a small error increase. Measurement variance was of the order of a few degrees, indicating that the method could reach this precision if all the sources of bias are removed. Thus, with optimised hardware, software and measurement protocol, the method is feasible for routine use in a clinical setting.Keywords:
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Artifact (error)
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.
Motor Imagery
Sensorimotor rhythm
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Idle
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Brain-computer Interface (BCI) provides a direct communication pathway for the brain and the outward environment. Specifically, motor imagery-based BCIs (MI-BCIs) has the advantage of actively outputting instructions without any external stimuli. Although this paradigm has been investigated for many years, individual MI-BCI is still of low performance. To this end, a collaborative strategy was proposed for MI-BCI system in this study. A20-channel EEG was adopted to inspect the classification performances of collaborative and individual MI-BCI. For 8 healthy subjects, four different motor imagery mental tasks (both hands, feet, left hand and right hand) were tested. Experimental results showed that, compared with that of individual system, the performance of MI-BCI with multiuser collaborative strategy could be improved by 16.5%. The proposed collaborative strategy could provide an available approach for BCI modulation and neural feedback research.
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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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최근 BCI(Brain Computer Interface) 기술의 발전과 함께 보급형 BCI 장치를 활용한 게임연구가 활발히 진행되고 있다. BCI 게임은 대부분 연구를 위한 실험적인 체험용 콘텐츠 형태로 개발되어 왔으며, 명령 패러다임에 있어서 BCI 게임 명령에 적합한 뇌파를 유도하는 방법에 대한 연구는 미흡하다. 본 연구에서는 음악의 리듬을 시청각적으로 표현하는 새로운 플레이 요소를 제공하는 BCI 리듬게임과 음악의 템포와 뇌파를 동기화 시켜 다양한 형태의 시청각 피드백을 생성하는 방법을 제안한다. 제안방법은 실험을 통해 게임조작에 필요로 하는 뇌파를 유도하여 게임점수를 향상시킬 수 있음을 확인하였다.
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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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