Wavlet phase-locking based binary classification of hand movement directions from EEG

Investigating the Application of Graph Theory Features in Hand Movement Directions Decoding using EEG Signals

In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.

Direction decoding of imagined hand movements using subject-specific features from parietal EEG

OBJECTIVE

Research on decoding brain signals for controlling external devices is rapidly emerging, owing to its versatile potential applications including neuro-prosthetic control and neurorehabilitation. Electroencephalogram (EEG)-based non-invasive Brain Computer Interface (BCI) systems decode brain signals to establish an augmented communication and control pathway between the brain and the computer. The development of an efficient BCI system requires accurate decoding of neural activity underlying user's intentions. This study investigates the directional tuning of Electroencephalogram (EEG) characteristics from posterior parietal region, associated with bidirectional hand movement imagination (motor imagery) in left and right directions.

APPROACH

The imagined movement directions of the chosen hand were decoded using a combination of envelope and phase features derived from parietal EEG of both hemispheres. The proposed algorithm uses wavelet for spectral decomposition, and discriminative subject-specific subband levels are identified based on Fisher analysis of envelope and phase features. The selected features from the discriminative subband levels are used for classifying left and right motor imagery directions of the hand using Support Vector Machine Classifier. Furthermore, the performance of the proposed algorithm is evaluated by incorporating a maximum variance-based EEG time bin selection algorithm.

MAIN RESULTS

With the time bin selection approach using subject-specific features, the proposed algorithm yielded an average left vs right motor imagery direction decoding accuracy of 73.33% across 15 healthy subjects. In addition, decoding accuracy offered by the phase features was higher than that of the envelope features, indicating the importance of phase features in MI kinematics decoding.

SIGNIFICANCE

The results reveal the significance of parietal EEG in decoding imagined kinematics and open new possibilities for future BCI research.

Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features

The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement

Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.

Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals

Decoding human movement parameters from electroencephalograms (EEG) signals is of great value for human-machine collaboration. However, existing studies on hand movement direction decoding concentrate on the decoding of a single-hand movement direction from EEG signals given the opposite hand is maintained still. In practice, the cooperative movement of both hands is common. In this paper, we investigated the neural signatures and decoding of single-hand and both-hand movement directions from EEG signals. The potentials of EEG signals and power sums in the low frequency band of EEG signals from 24 channels were used as decoding features. The linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used for decoding. Experimental results showed a significant difference in the negative offset maximums of movement-related cortical potentials (MRCPs) at electrode Cz between single-hand and both-hand movements. The recognition accuracies for six-class classification, including two single-hand and four both-hand movement directions, reached 70.29%± 10.85% by using EEG potentials as features with the SVM classifier. These findings showed the feasibility of decoding single-hand and both-hand movement directions. This work can lay a foundation for the future development of an active human-machine collaboration system based on EEG signals and open a new research direction in the field of decoding hand movement parameters from EEG signals.