Enhance the Classification Methods for Neurological Signals in Motor Imagery BCI Systems
Keywords:
Brain-Computer Interface (BCI), Electroencephalography (EEG), Motor Imagery (MI), Electrocorticography (ECoG), Signal, Deep learning (DL), Feature extraction, Wavelet packet decomposition (WPD), Sensorimotor Rhythms (SMR), Machine Learning (ML), feature extraction, low-resolution.Abstract
Brain-Computer Interfaces (BCIs) are pioneering advancements in medical, neurological, and rehabilitation fields by merging insights from various disciplines. Motor Imagery (MI) is particularly promising for enhancing mobility in individuals with impairments. This study offers a comprehensive review of signal processing methods and electroencephalography (EEG) techniques used in MI-based BCI training. It encompasses the full process from EEG signal acquisition to preprocessing, feature extraction, and classification. Integrating machine learning (ML) and deep learning (DL) techniques significantly improves the accuracy and efficiency of BCI motor imagery signal classification, facilitating real-time neurofeedback applications. A crucial element of MI-BCIs is detecting Sensorimotor Rhythms (SMR) during motor imagery tasks, which indicate changes in brain activity linked to movement intentions. To tackle challenges related to low-resolution EEG signals, this research introduces a standardized MI-BCI reporting format. Additionally, novel algorithms for feature extraction and classification are proposed based on data from 10 participants performing four distinct MI tasks using scalp electrodes. The study also assesses the hardware and signal processing capabilities required for MI-BCI data collection, highlighting current technological limitations and opportunities for enhancement. In conclusion, the study anticipates continued advancements in EEG-BCI research, emphasizing the potential of these technologies to revolutionize clinical practices and improve the quality of life for individuals with neurological conditions.
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