Yijun Zou

TL;DR: A novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG and the results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance.

TL;DR: Compared with CSP and FBCSP features, the proposed approach can significantly increase the decoding accuracy for multiclass MI tasks from the same upper limb and could potentially be applied in the context of MI-based BMI control of a robotic arm or a neural prosthesis for motor disabled patients with highly impaired upper limb.

TL;DR: A new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects is proposed and achieves a satisfactory recognition accuracy using very few training trials.

TL;DR: This study gives a preliminary evidence that the integrated robot-assisted rehabilitation system combined with SSVEP-based BCI will make future rehabilitation therapy more effective.

TL;DR: Wang et al. as mentioned in this paper proposed a supervised method to extract the independent components corresponding to different motion imagery (MI) activities in the brain, by designing a new optimization objective and solving it, they combine the idea of ICA with principle of MI in an individual algorithm.