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This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After...
This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with...
The online performance of a motor imagery-based Brain-Computer Interface (MI-BCI) influences its effectiveness and usability in real-world clinical applications such as the restoration of motor control. The online performance depends on factors such as the different feedback techniques and motivation of the subject. This paper investigates the online performance of the MI-BCI with an augmented-reality...
Non-invasive EEG-based motor imagery brain-computer interface (MI-BCI) holds promise to effectively restore motor control to stroke survivors. This clinical study investigates the effects of MI-BCI for upper limb robotic rehabilitation compared to standard robotic rehabilitation. The subjects are hemiparetic stroke patients with mean age of 50.2 and baseline Fugl-Meyer (FM) score 29.7 (out of 66,...
The filter bank common spatial pattern (FBCSP) algorithm performs autonomous selection of key temporal-spatial discriminative EEG characteristics in motor imagery-based brain computer interfaces (MI-BCI). However, FBCSP is sensitive to outliers because it involves multiple estimations of covariance matrices from EEG measurements. This paper proposes a Robust FBCSP (RFBCSP) algorithm whereby the estimates...
This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence,...
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