Kapeller C, Gergondet P, Kamada K, Ogawa H, Takeuchi F, Ortner R, Pruckl R, Kheddar A, Scharinger J, Guger C
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1765-8. doi: 10.1109/EMBC.2015.7318720.
Intention recognition through decoding brain activity could lead to a powerful and independent Brain-Computer-Interface (BCI) allowing for intuitive control of devices like robots. A common strategy for realizing such a system is the motor imagery (MI) BCI using electroencephalography (EEG). Changing to invasive recordings like electrocorticography (ECoG) allows extracting very robust features and easy introduction of an idle state, which might simplify the mental task and allow the subject to focus on the environment. Especially for multi-channel recordings like ECoG, common spatial patterns (CSP) provide a powerful tool for feature optimization and dimensionality reduction. This work focuses on an invasive and independent MI BCI that allows triggering from an idle state, and therefore facilitates tele-operation of a humanoid robot. The task was to lift a can with the robot's hand. One subject participated and reached 95.4 % mean online accuracy after six runs of 40 trials. To our knowledge, this is the first online experiment with a MI BCI using CSPs from ECoG signals.
通过解码大脑活动进行意图识别，有望构建强大且独立的脑机接口（BCI），实现对机器人等设备的直观控制。实现此类系统的常用策略是基于脑电图（EEG）的运动想象（MI）脑机接口。改用侵入性记录方式，如皮层脑电图（ECoG），能够提取非常稳健的特征，并易于引入空闲状态，这可能会简化心理任务，使受试者能够专注于周围环境。特别是对于像ECoG这样的多通道记录，共同空间模式（CSP）为特征优化和降维提供了强大的工具。这项工作聚焦于一种侵入性且独立的运动想象脑机接口，该接口允许从空闲状态触发，从而便于对人形机器人进行远程操作。任务是用机器人的手拿起一个罐子。一名受试者参与实验，在进行了六组、每组40次试验后，平均在线准确率达到了95.4%。据我们所知，这是首次使用来自ECoG信号的CSP进行运动想象脑机接口的在线实验。
