English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Non-invasive brain-computer interfaces help the subjects to control externaldevices by brain intentions. The multi-class classification of upper limbmovements can provide external devices with more control commands. The onsetsof the upper limb movements are located by the external limb trajectory toeliminate the delay and bias among the trials. However, the trajectories arenot recorded due to the limitation of experiments. The delay cannot be avoidedin the analysis of signals. The delay negatively influences the classificationperformance, which limits the further application of upper limb movements inthe brain-computer interface. This work focuses on multi-channel brain signalsanalysis in the temporal-frequency approach. It proposes the two-stage-trainingtemporal-spectral neural network (TTSNet) to decode patterns from brainsignals. The TTSNet first divides the signals into various filter banks. Ineach filter bank, task-related component analysis is used to reduce thedimension and reject the noise of the brain. A convolutional neural network(CNN) is then used to optimize the temporal characteristic of signals andextract class-related features. Finally, these class-related features from allfilter banks are fused by concatenation and classified by the fully connectedlayer of the CNN. The proposed method is evaluated in two public datasets. Theresults show that TTSNet has an improved accuracy of 0.7456$\pm$0.1205 comparedto the EEGNet of 0.6506$\pm$0.1275 ($p<0.05$) and FBTRCA of 0.6787$\pm$0.1260($p<0.1$) in the movement detection task, which classifies the movement stateand the resting state. The proposed method is expected to help detect limbmovements and assist in the rehabilitation of stroke patients.

Enabling Temporal-Spectral Decoding in Pre-movement Detection