Search Results (2)

The unilateral limb motor imagery paradigm can effectively address the cognitive dissociation problem among multiple limbs and provide strong technical support for extending the functionality of external devices. However, feature mining and accurate decoding of unilateral limb movements remain challenging. In this study, we propose a feature mining method that combines automatic frequency band boundary localization with regularized common spatial pattern (AFBBL-RCSP), and employ a pinball-loss-based twin support vector machine (Pin-UTSVM) to decode EEG signals corresponding to reaching, turning, and grasping movements. First, multiple optimal frequency band boundaries were identified for each subject using AFBBL. Then, regularized spatial features were extracted from each sub-band, and all features were reduced using Fisher’s discriminant analysis. Finally, the Pin-UTSVM classifier was used to categorize the three types of movement data. The results show that, compared with CSP and RCSP feature mining methods using the fixed 8–30 Hz band, the proposed method improves decoding accuracy by 9.52% and 3.89%, respectively. Compared with fixed single-band feature mining methods based on the α band, β band, and α + β band, the proposed method improves accuracy by 5.56%, 3.89%, and 3.73%, respectively. In addition, compared with existing unilateral limb decoding methods based on temporal-spatial features, temporal-frequency features, and temporal-spatial-temporal-frequency fusion CNN features, the proposed method improves decoding accuracy by 34.93%, 34.09%, and 28.11%, respectively. These results suggest that the proposed AFBBL-RCSP method is effective for unilateral limb motor imagery EEG decoding. Full article

The automatic localization of audio sources distributed symmetrically with respect to coronal or transverse planes using binaural signals still poses a challenging task, due to the front–back and up–down confusion effects. This paper demonstrates that the convolutional neural network (CNN) can be used to automatically localize music ensembles panned to the front, back, up, or down positions. The network was developed using the repository of the binaural excerpts obtained by the convolution of multi-track music recordings with the selected sets of head-related transfer functions (HRTFs). They were generated in such a way that a music ensemble (of circular shape in terms of its boundaries) was positioned in one of the following four locations with respect to the listener: front, back, up, and down. According to the obtained results, CNN identified the location of the ensembles with the average accuracy levels of 90.7% and 71.4% when tested under the HRTF-dependent and HRTF-independent conditions, respectively. For HRTF-dependent tests, the accuracy decreased monotonically with the increase in the ensemble size. A modified image occlusion sensitivity technique revealed selected frequency bands as being particularly important in terms of the localization process. These frequency bands are largely in accordance with the psychoacoustical literature. Full article