Improving pre-movement pattern detection with filter bank selection

HAO JIA; FENG DUAN; YU ZHANG; CESAR F. CAIAFA; JORDI SOLE-CASALS

Journal of Neural Engineering

IOP SCIENCE

Año: 2022

1741-2552

Objective: Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states. Approach: The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract canonical correlation patterns. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) was also used to select canonical correlation patterns. Results: Three methods were evaluated using EEG signals in the readiness potential section. The readiness potential section is a two-second time window before the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8666±0.1038 while the accuracies of STRCA and CNN were 0.8287±0.1101 and 0.8501±0.1049, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.5970±0.1424, 0.6112±0.1438, 0.6216±0.1409, respectively. Feature selection using filter banks, as in FBTRCA, improves on the classification performance of STRCA. Significance: The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.