A Comprehensive Approach for Enhancing Motor Imagery EEG Classification in BCI’s

Muhammad Tariq Sadiq, Siuly Siuly, Yan Li, Paul Wen

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


Electroencephalography (EEG) based on motor imagery has become a potential modality for brain-computer interface (BCI) systems, allowing users to control external devices by imagining doing particular motor activities. The existence of noise and the complexity of the brain signals, however, make it difficult to classify motor imagery EEG signals. This work suggests a systematic method for classifying motor imagery in the EEG. A technique known as Multiscale Principal Component Analysis (MSPCA) is used for efficient noise removal to improve the signal quality. A unique signal decomposition technique is proposed for modes extraction, allowing the separation of various oscillatory components related to motor imagery tasks. This breakdown makes it easier to isolate important temporal and spectral properties that distinguish various classes of motor imagery. These characteristics capture the dynamism and discriminative patterns present in motor imagery tasks. The motor imagery EEG signals are then classified using various machine learning and deep learning-based models based on the retrieved features. The findings of the classification show how well the suggested strategy works in generating precise and trustworthy classification success for various motor imaging tasks. The proposed method has enormous potential for BCI applications, allowing people with motor limitations to operate extrasensory equipment via brain signals.

Original languageEnglish
Title of host publicationHealth Information Science
Subtitle of host publication12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings
EditorsYan Li, Zhisheng Huang, Manik Sharma, Lu Chen, Rui Zhou
PublisherSpringer, Singapore
Number of pages14
ISBN (Print)9789819971077
Publication statusPublished - 11 Nov 2023
Event12th International Conference on Health Information Science, HIS 2023 - Melbourne, Australia
Duration: 23 Oct 202324 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14305 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Health Information Science, HIS 2023

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.


  • Brain-Computer Interface
  • Classification
  • Motor Imagery EEG
  • Signal Decomposition


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