BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation

Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robots. However, surface EEG-based early KP estimation studies are sparse in the literature. In this study, simultaneous surface EEG and kinematics data of five par...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2023-12, p.1-1
Main Authors: Saini, Manali, Jain, Anant, Muthukrishnan, Suriya Prakash, Bhasin, Shubhendu, Roy, Sitikantha, Kumar, Lalan
Format: Article
Language:English
Subjects:
Brain-computer interface
Decoding
Deep learning
Discrete Fourier transforms
Discrete wavelet transform
EEG
Electroencephalogram
Electroencephalography
Estimation
Feature extraction
Head harmonics
Kinematic parameter estimation
Spherical harmonics
Task analysis
Trajectory
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robots. However, surface EEG-based early KP estimation studies are sparse in the literature. In this study, simultaneous surface EEG and kinematics data of five participants is collected during the biceps-curl motor task. The feasibility of early estimation of KPs is demonstrated using brain source imaging (BSI). Discrete wavelet transform (DWT) is utilized for sub-band extraction from pre-processed EEG signals. Further, spherical and head harmonics domain features are extracted from sub-bands of the EEG signals. A deep-learning based decoding model, BiCurNet, is proposed for early KP estimation using spatial and harmonics domain EEG features during the biceps-curl task. The proposed model utilizes lightweight architecture with depth-wise separable convolution layers and a customized attention module. The best Pearson correlation coefficient (PCC) between the estimated and actual trajectory of 0.7 is achieved when combined EEG features (spatial and harmonics domain) in the delta band are utilized. Intra- and Inter-subject performance analyses are performed to evaluate the subject-adaptability of the proposed decoding model. The performance of the proposed BiCurNet is compared with the existing multi-linear regression (mLR) counterpart. The robustness of the proposed model is additionally illustrated using an ablation study. The robust performance and lightweight architecture will facilitate the real-time implementation of the model with deployment on a microcontroller to control BCI-based wearable robots.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3346505