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

The Brain-Computer Interface system is a profoundly developing area ofexperimentation for Motor activities which plays vital role in decodingcognitive activities. Classification of Cognitive-Motor Imagery activities fromEEG signals is a critical task. Hence proposed a unique algorithm forclassifying left/right-hand movements by utilizing Multi-layer PerceptronNeural Network. Handcrafted statistical Time domain and Power spectral densityfrequency domain features were extracted and obtained a combined accuracy of96.02%. Results were compared with the deep learning framework. In addition toaccuracy, Precision, F1-Score, and recall was considered as the performancemetrics. The intervention of unwanted signals contaminates the EEG signalswhich influence the performance of the algorithm. Therefore, a novel approachwas approached to remove the artifacts using Independent Components Analysiswhich boosted the performance. Following the selection of appropriate featurevectors that provided acceptable accuracy. The same method was used on all ninesubjects. As a result, intra-subject accuracy was obtained for 9 subjects94.72%. The results show that the proposed approach would be useful to classifythe upper limb movements accurately.

Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis