PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task

TL;DR

This study demonstrates that pre-movement delta-band EEG signals can be used with deep learning decoders to accurately predict 3D hand kinematics for grasp and lift tasks, enhancing BCI control.

Contribution

Introduces PreMovNet neural decoders that leverage pre-movement EEG for motor trajectory decoding, outperforming traditional linear models.

Findings

Decoding of 3D hand kinematics from pre-movement EEG is viable.

Deep learning decoders outperform linear regression models.

Pre-movement EEG signals enable better BCI control.

Abstract

Kinematics decoding from brain activity helps in developing rehabilitation or power-augmenting brain-computer interface devices. Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associated with the neural motor correlation utilised for motor trajectory decoding (MTD). In this communication, the ability to decode motor kinematics trajectory from pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy participants. In particular, two deep learning-based neural decoders called PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related neural information existing in the pre-movement EEG data. EEG data segments with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the movement onset are utilised for the same. The MTD is presented for grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken as input to the neural decoders. The performance of the proposed decoders are compared with the state-of-the-art multi-variable linear regression (mLR) model. Pearson correlation coefficient and hand trajectory are utilised as performance metric. The results demonstrate the viability of decoding 3D hand kinematics using pre-movement EEG data, enabling better control of BCI-based external devices such as exoskeleton/exosuit.