Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la, Universidad S/N, Ed. Innova, Elche, Alicante, 03202, Spain.
Int J Neural Syst. 2021 Nov;31(11):2150015. doi: 10.1142/S0129065721500155. Epub 2021 Feb 26.
Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.
脑机接口（BCI）正成为运动障碍患者康复过程中的一种重要技术工具，因为它们能够通过促进神经可塑性来恢复大脑和四肢之间的连接。它们可以用作辅助设备来提高残疾人士的活动能力。出于这个原因，当前的 BCI 必须进行改进，以允许对外置设备进行准确和自然的使用。这项工作提出了一种基于事件相关去同步（ERD）检测行走时改变方向意图的新方法。对脑电图（EEG）信号的频率和时频特征进行了特征描述。然后，选择最有影响力的特征和电极，将方向改变意图与行走区分开来。当将频率和时频特征与平均准确率为[Formula: see text]%相结合时，可获得最佳结果，这有望应用于未来的 BCI。
