Multi-class classification of upper limb movements with filter bank task-related component analysis

Hao Jia ... [et al.].

Serie: Trabajos publicados del IAR ; no. 1741

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 commands 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 movements. 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, similarity measuring and filter bank selection. The spatial filter, namely the task-related com- ponent analysis, is first used to remove noise from EEG signals. The canonical correlation 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 essen- tial features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used mod- els is evaluated using two datasets. mFBTRCA achieved a classi- fication accuracy of 0.4193±0.0780 (7 classes) and 0.4032±0.0714 (5 classes), respectively, which improves on the best accura- cies 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.