Parallel Factorization to Implement Group Analysis in Brain Networks Estimation.

When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation.

Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.

BACKGROUND: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks. METHODS: EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups. RESULTS: Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients. CONCLUSIONS: High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients.

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.

Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface.

Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.