A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.
A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.
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DOI：
10.1109/iembs.2011.6091576
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Schiff SJ
中科院分区：
文献类型：
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作者：
Kamrunnahar M;Schiff SJ
本文研究了一种非线性集成卡尔曼滤波器（SPKF）在运动图像脑机接口（BCI）中的应用。利用头皮脑电图（EEG）信号，将平方根中心差分卡尔曼滤波（SR-CDKF）作为运动想象任务执行中大脑状态估计的方法。在记录头皮脑电图信号的同时，健康受试者想象左手和右手的运动以及舌头和双侧脚趾的运动。离线数据分析用于训练模型以及解码图像运动。初步结果表明，该方法对手部动作的解码准确率为78% ~ 90%，对舌趾动作的解码准确率为70% ~ 90%。正在进行的研究包括该方法的在线BCI应用，以及将该算法用于不同系统动态模型的组合状态和参数估计。
We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.