脑-机接口
计算机科学
脑磁图
手势
卷积神经网络
皮质电图
人工智能
解码方法
深度学习
接口(物质)
预处理器
模式识别(心理学)
语音识别
脑电图
神经科学
心理学
电信
气泡
最大气泡压力法
并行计算
作者
Yifeng Bu,Deborah L. Harrington,Roland R. Lee,Qian Shen,Annemarie Angeles‐Quinto,Zhengwei Ji,Hayden Hansen,Jaqueline Hernandez-Lucas,J. D. Baumgartner,Tao Song,Sharon Nichols,Dewleen G. Baker,Ramesh Rao,Imanuel Lerman,Tuo Lin,Xin Tu,Mingxiong Huang
出处
期刊:Cerebral Cortex
[Oxford University Press]
日期:2023-05-13
卷期号:33 (14): 8942-8955
被引量:9
标识
DOI:10.1093/cercor/bhad173
摘要
Abstract Advancements in deep learning algorithms over the past decade have led to extensive developments in brain–computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.
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