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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature using in brain-computer interface (BCI) provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on the corresponding frequency emphasize method to decode the motor imagery (MI) data from the 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely &#x2018;Cylindrical&#x2019;, &#x2018;Spherical&#x2019;, and &#x2018;Lumbrical&#x2019;. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results showed that the MI-related frequency range was activated during the MI task, and provide neurophysiological evidence to design the proposed method. When using the proposed method, the average classification performance in the intra-session condition was 69.68% and the average classification performance in the inter-session condition was 52.76%. Our results provided the possibility of developing a BCI-based device control system for practical applications.

Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework