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BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications | |
Avinash Singh1; Tao X(陶显)2![]() | |
2021-01 | |
会议名称 | 2020 IEEE Symposium Series on Computational Intelligence (SSCI). |
会议日期 | 2021-1 |
会议地点 | 澳大利亚 |
摘要 | EEG based brain-computer interface (BCI) allows people to communicate and control external devices using brain signals. The application of BCI ranges from assisting in disabilities to interaction in a virtual reality environment by detecting user intent from EEG signals. The major problem lies in correctly classifying the EEG signals to issue a command with minimal requirement of pre-processing and resources. To overcome these problems, we have proposed, BCINet, a novel optimized convolution neural network model. We have evaluated the BCINet over two EEG based BCI datasets collected in mobile brain/body imaging (MoBI) settings. BCINet significantly outperforms the classification for two datasets with up to 20% increase in accuracy while fewer than 75% trainable parameters. Such a model with improved performance while less requirement of computation resources opens the possibilities for the development of several real-world BCI applications with high performance. |
收录类别 | EI |
七大方向——子方向分类 | 脑机接口 |
国重实验室规划方向分类 | 多尺度信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57212 |
专题 | 中科院工业视觉智能装备工程实验室_精密感知与控制 |
作者单位 | 1.University of Technology Sydney 2.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Avinash Singh,Tao X. BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
BCINet_An_Optimized_(305KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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