scholarly journals Subject-to-subject adaptation to reduce calibration time in motor imagery-based brain-computer interface

2014 ◽  
Author(s):  
Mahnaz Arvaneh ◽  
Ian Robertson ◽  
Tomas E. Ward
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Calibration Time

scholarly journals Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yilu Xu ◽  
Jing Hua ◽  
Hua Zhang ◽  
Ronghua Hu ◽  
Xin Huang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Optimization Problem ◽  
Brain Computer Interface ◽  
The Other ◽  
Computer Interface ◽  
Geometric Feature ◽  
Support Vector ◽  
Transductive Support Vector Machine ◽  
Calibration Time

Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.

scholarly journals Reduce Calibration Time in Motor Imagery Using Spatially Regularized Symmetric Positives-Definite Matrices Based Classification

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 379 ◽  
Author(s):  
Amardeep Singh ◽  
Sunil Lal ◽  
Hans Guesgen
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Spatial Filter ◽  
Covariance Matrices ◽  
Small Sample ◽  
Computer Interface ◽  
Limited Applicability ◽  
Calibration Time ◽  
Electroencephalogram Eeg ◽  
New Framework

Electroencephalogram (EEG) based motor imagery brain–computer interface (BCI) requires large number of subject specific training trials to calibrate the system for a new subject. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of a brain–computer interface is to reduce calibration time or completely eliminate it. To address this problem, existing approaches use covariance matrices of electroencephalography (EEG) trials as descriptors for decoding BCI but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing SPD based classification approach. However, SPD-based classification has limited applicability in small training sets because the dimensionality of covariance matrices is large in proportion to the number of trials. To overcome this drawback, our paper proposes a new framework that transforms SPD matrices in lower dimension through spatial filter regularized by prior information of EEG channels. The efficacy of the proposed approach was validated on the small sample scenario through Dataset IVa from BCI Competition III. The proposed approach achieved mean accuracy of 86.13 % and mean kappa of 0.72 on Dataset IVa. The proposed method outperformed other approaches in existing studies on Dataset IVa. Finally, to ensure the robustness of the proposed method, we evaluated it on Dataset IIIa from BCI Competition III and Dataset IIa from BCI Competition IV. The proposed method achieved mean accuracy 92.22 % and 81.21 % on Dataset IIIa and Dataset IIa, respectively.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information
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