Exploring EEG spectral and temporal dynamics underlying a hand grasp movement

Abstract

For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex’s functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

Analysis of Minimal Channel Electroencephalography for Wearable Brain–Computer Interface

“…The spatial distribution measured with EEG was reported to vary during movement-related activity, depending on the frequency band [74][75][76]. Our results indicated that the changes in EEG occurred from the frontal (F3, Fz, F4) to parietal (P3, Pz, P4) regions, which is consistent with many studies [62,77,78]. In addition, the results from these studies [36,51] show that filtering brain rhythms into a frequency filter (FB) provides good classification accuracy.…”

Libet’s legacy: A primer to the neuroscience of volition

“…More recently, algorithms based on modern machine-learning methods have become increasingly capable of predicting movement or its properties (such as direction) from EEG with better-than-chance prediction accuracy, even in real time (e.g., Bai et al, 2011;Gheorghe et al, 2013;Salvaris & Haggard, 2014). These methods typically use EEG markers, such as ERD or LRP (see sections 4.1.1 and 4.1.2), and are continuously improved (Abou Zeid & Chau, 2015;Bodda & Diwakar, 2022;Hasan et al, 2020;Lashgari et al, 2020Lashgari et al, , 2021Lin et al, 2016). In addition, single-trial movement predictions based on intracranial recordings (see section 4.1.3) are typically even more accurate and allow predictions earlier in time (Aflalo et al, 2022;Fried et al, 2011), including online and in real-time (Maoz et al, 2012).…”