本文選題：腦機接口 + 康復(fù)訓(xùn)練　； 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：中風(fēng)患者普遍存在一定程度上的運動功能障礙，，如何增強其肢體肌肉力量一直是康復(fù)訓(xùn)練學(xué)科的重要問題。然而，傳統(tǒng)的康復(fù)訓(xùn)練方法效果極其有限。腦機接口（BCIs）是一種能夠連接外部設(shè)備與人類大腦的技術(shù)，使得人類能夠運用自己的思維來直接控制外部設(shè)備，而不需要肌肉、軀干的參與。因此，腦機接口可以被用于重建中風(fēng)受損腦區(qū)，即利用運動想象與神經(jīng)反饋增強運動控制網(wǎng)絡(luò)重建的學(xué)習(xí)，恢復(fù)受損的運動控制功能。 本論文首先介紹了基于腦機接口技術(shù)的康復(fù)訓(xùn)練系統(tǒng)；我們與復(fù)旦大學(xué)華山醫(yī)院康復(fù)科合作，進行臨床試驗，采集EEG康復(fù)數(shù)據(jù)，建立數(shù)據(jù)庫，并針對BCI康復(fù)數(shù)據(jù)進行分析，揭示基于運動想象的康復(fù)訓(xùn)練過程中的相關(guān)康復(fù)機理。我們提出了一套康復(fù)訓(xùn)練評價體系，并對傳統(tǒng)的臨床康復(fù)訓(xùn)練療程和基于腦機接口的康復(fù)訓(xùn)練療程進行了比較和評估。我們研究了腦機接口技術(shù)對于患者康復(fù)訓(xùn)練所起的作用，并通過比較得到了相應(yīng)的定性和定量分析結(jié)果。 此外，本論文提出了一種基于高斯混合模型和弱監(jiān)督學(xué)習(xí)的二類運動想象分類算法。相比較于傳統(tǒng)的公共空間模式算法，該算法能夠更好的處理低信噪比的中風(fēng)病人腦電信號。通過與傳統(tǒng)算法的定量比較，我們驗證了該算法對于中風(fēng)病人腦電信號的適用性和有效性。通過對中風(fēng)病人的腦電信號建立混合模型，我們進一步挖掘了中風(fēng)病人康復(fù)過程中的腦區(qū)變化機理。
[Abstract]:Stroke patients generally have a certain degree of motor dysfunction, how to enhance their limb muscle strength has been an important issue in rehabilitation training. However, the effect of traditional rehabilitation training is extremely limited. Brain-Computer Interface (BCIs) is a technology that can connect external devices with human brain, which enables people to use their own thinking to directly control external devices without the participation of muscles and torso. Therefore, brain-computer interface can be used to reconstruct the damaged brain area of stroke, that is, using motor imagination and neural feedback to enhance the learning of motor control network reconstruction and restore the damaged motor control function. This paper first introduces the rehabilitation training system based on brain-computer interface technology. In cooperation with the Department of Rehabilitation of Huashan Hospital, Fudan University, we conduct clinical trials, collect EEG rehabilitation data, set up a database, and analyze BCI rehabilitation data. To reveal the mechanism of rehabilitation training based on sports imagination. We put forward a set of evaluation system of rehabilitation training, and compared and evaluated the traditional course of clinical rehabilitation training and the course of rehabilitation training based on brain-computer interface. We studied the effect of brain-computer interface technology on rehabilitation training of patients, and obtained the corresponding qualitative and quantitative results by comparison. In addition, this paper proposes a classification algorithm based on Gao Si hybrid model and weakly supervised learning. Compared with the traditional common space mode algorithm, the proposed algorithm can deal with the EEG signals of stroke patients with low SNR. Through quantitative comparison with the traditional algorithm, we verify the applicability and effectiveness of the algorithm to stroke patients. By establishing a mixed model of EEG in stroke patients, we further explore the mechanism of brain region change during rehabilitation of stroke patients.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7;R743.3

【參考文獻】

相關(guān)期刊論文 前1條

1 CICHOCKI Andrzej;;EEG-based asynchronous BCI control of a car in 3D virtual reality environments[J];Chinese Science Bulletin;2009年01期

本文編號：2090818