图 书 馆
面向脑机接口的机器学习Machine Learning for Brain-Computer Interfaces |
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| 课程网址: | http://videolectures.net/nips09_hill_mlb/ |
| 主讲教师: | Jeremy Hill |
| 开课单位: | 马克斯普朗克生物控制论研究所 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user's intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn't matter what classifier you use once you've done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I'll highlight some of the interesting problems that remain open for machine-learners. |
| 关 键 词: | 脑机接口; 运动功能; 临床应用 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-04：chenjy |
| 最后编审: | 2022-12-04：chenjy |
| 阅读次数: | 40 |