Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasp...

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Bibliographic Details
Main Authors: Rabiee, Ali, Ghafoori, Sima, Cetera, Anna, Shahriari, Yalda, Abiri, Reza
Format: Conference Proceeding
Language:English
Subjects:
Alpha and Beta Frequency Bands
and Machine Learning
EEG
Electroencephalography
Engineering in medicine and biology
Grasping
Hand Grasp Classification
Machine learning
Motor Cortex Activity
Motors
Neuroprostheses
Real-time systems
Signal processing
Time-frequency analysis
Wavelet analysis
Wavelet Signal Processing
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Summary:This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.
ISSN:2694-0604
DOI:10.1109/EMBC53108.2024.10782674