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Electrooculogram (EOG) contamination is a common critical issue in general EEG studies as well as in building highperformance brain computer interfaces (BCI). Existing regression or independent component analysis based artefacts correction methods are usually not applicable when EOG is not available or when there are very few EEG channels. In this paper, we propose a novel ocular artefacts correction...
An electroencephalography (EEG)-based Motor Imagery Brain-Computer Interface (MI-BCI) requires a long setup time if a large number of channels is used, and EEG from noisy or irrelevant channels may adversely affect the classification performance. To address this issue, this paper proposed 2 approaches to systematically select discriminative channels for EEG-based MI-BCI. The proposed Discriminative...
The aim of the present study was to analysis nonlinear dependence of the electroencephalogram (EEG) during photon stimulation (during-PS) with cross mutual information (CMI). EEGs from during-PS and pre-PS were recorded in the 18 age-matched controls. Statistically significant differences were found for between during-PS and pre-PS using a nonparametric Wilcoxon (matched-pairs) signed-rank test in...
EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable,...
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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