Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis

Hao Jia ... [et al.].

Serie: Trabajos publicados del IAR ; no. 1756

Resumen: The classification of limb movements can provide with control commands in non-invasive brain- computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control com- mands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the- art method in the multi-class classification of limb move- ments. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, sim- ilarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correla- tion measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the es- sential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previ- ously used models is evaluated using two datasets. mFB- TRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.