A review on brain computer interfaces: contemporary achievements and future goals towards movement restoration

Restoration of motor functions of patients with loss of mobility constitutes a yet unsolved medical problem, but also one of the most prominent research areas of neurosciences. Among suggested solutions, Brain Computer Interfaces have received much attention. BCI systems use electric, magnetic or metabolic brain signals to allow for control of external devices, such as wheelchairs, computers or neuroprosthetics, by disabled patients. Clinical applications includespinal cord injury, cerebrovascular accident rehabilitation, Amyotrophic Lateral Sclerosis patients. Various BCI systems are under re­search, facilitated by numerous measurement techniques including EEG, fMRI, MEG, nIRS and ECoG, each with its own advantages and disadvantages.Current research effort focuses on brain signal identification and extraction. Virtual Reality environments are also deployed for patient training. Wheelchair or robotic arm control has showed up as the first step towards actual mobility restoration. The next era of BCI research is envisaged to lie along the transmission of brain signals to systems that will control and restore movement of disabled patients via mechanical appendixes or directly to the muscle system by neurosurgical means

A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury

Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients

Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement:A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Introduction . Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. Materials and Methods . Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome. Results and Discussion . Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution. Conclusions . Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces

FCLAB:An EEGLAB module for performing functional connectivity analysis on single-subject EEG data

Functional connectivity (FC) analysis constitutes a fundamental neuroscientific approach that has been extensively used for the investigation of brain's connectivity and activation patterns. To that end, several software tools have been developed. This paper presents FCLAB, the only EEGLAB-based plugin, which is able to work with EEG signals in order to estimate and visualize brain functional connectivity networks based on a variety of similarity measures as well as run a complete graph analysis procedure followed by a detailed visualization of the ensuing local and global measures distribution. FCLAB entails optimization procedures for the implementation of the connectivity structures and is the result of long-term research in EEG functional connectivity. The computed functional connectivity measures have been carefully selected to reflect the state-of-art in the field. Future work focuses on extending the platform for multi-subject analysis in order to enable the implementation of statistical analysis tools

Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity?

Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design.Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs).Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions.Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT02991781

Study of neuronal circuits of the brain in patients with spinal cord injury

Neurotrauma of ascending and descending pathways, caused by Spinal Cord Injury (SCI), compromises reciprocal communication of the central and peripheral nervous systems. Complete or incomplete SCI results in varying degree of motor, sensory and autonomic impairment and long-lasting, often irreversible disability. Changes in brain organization and structure have been associated with SCI and have often been associated with differences in prognosis. Changes in functional connectivity (FC) could serve as injury biomarkers. Most studies on FC after SCI have focused on chronic complete injury or resting state condition. In addition, SCI patients have started to increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for SCI patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoF). In our study, ten right-handed patients with incomplete SCI (one complete) and ten age- and gender- matched healthy controls were recruited. They performed multiple classes of visual motor tasks of the upper extremities, as well as walking, while under high-resolution Electroencepha-lographic (EEG) recording. The subjects also operated, in a single session, anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform different upper extremity movements that corresponded to the visual motor imagery classes. Those classes were grouped into categories, used both in network and BCI analysis. Directed Transfer Function was used to study FC at the cortical source space between nodes of sensorimotor network. Network-based statistics and graph analysis were used to study the functional networks. Also, BCI training skill and BCI control performance were analyzed with regards to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. A subset of the sensorimotor network including primary motor and somatosensory areas showed significantly reduced connectivity power in SCI subjects, regardless of positive or negative neurological outcome. Cingulate motor areas (CMA) consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. SCI subjects had higher outflow from left CMA than healthy subjects and higher inflow in right CMA than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. SCI group showed signs of increased local processing as an adaptive mechanism. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Chronic disruption of reciprocal communication in incomplete SCI can result in permanent significant stress on the connectivity of the sensorimotor network and the emergence of adaptive neural plasticity mechanisms.Ο τραυματισμός των ανιόντων και κατιόντων νευρικών οδών, που προκαλείται από τη κάκωση του νωτιαίου μυελού (ΚΝΜ), ζημιώνει την αμοιβαία επικοινωνία του κεντρικού και του περιφερικού νευρικού συστήματος. Η πλήρης ή ατελής ΚΝΜ ευθύνεται για κινητική, αισθητική και αυτονομική βλάβη μακράς διάρκειας η οποία μπορεί να ποικίλει ως προς το βαθμό βαρύτητας και συχνά οδηγεί σε μη αναστρέψιμη αναπηρία. Οι αλλαγές στην οργάνωση και τη δομή του εγκεφάλου σχετιζόμενες με ΚΝΜ έχουν συχνά συσχετιστεί με διαφορές στην πρόγνωση. Οι αλλαγές στη λειτουργική συνδεσιμότητα θα μπορούσαν να λειτουργήσουν ως βιοδείκτες του τραυματισμού. Οι περισσότερες μελέτες λειτουργικής συνδεσιμότητας μετά από ΚΝΜ επικεντρώθηκαν στη χρόνια πλήρη κάκωση ή σε δραστηριότητα ηρεμίας. Επιπλέον, οι ασθενείς με ΚΝΜ έχουν αρχίσει να επωφελούνται όλο και περισσότερο από την βοηθητική ρομποτική τεχνολογία. Το πεδίο των διεπαφών υπολογιστή-εγκεφάλου (ΔΥΕ) έχει αρχίσει να αναπτύσσει ώριμες βοηθητικές εφαρμογές για ασθενείς με ΚΝΜ. Παρ’ όλα αυτά, οι μη επεμβατικές ΔΥΕ εξακολουθούν να στερούνται ελέγχου ακριβείας εξωτερικών συσκευών πολλών βαθμών ελευθερίας. Στη μελέτη μας στρατολογήθηκαν δέκα δεξιόχειρες ασθενείς με ατελή ΚΝΜ (ένας με πλήρη) και δέκα υγιείς μάρτυρες αντίστοιχου ηλικίας και φύλου. Πραγματοποίησαν πολλαπλές κλάσεις οπτικής νοερής κίνησης (ΟΝΚ) των άνω άκρων και βάδισης, υπό ηλεκτροεγκεφαλογραφική καταγραφή υψηλής ανάλυσης. Οι συμμετέχοντες επίσης χρησιμοποίησαν, σε μία μόνο συνεδρία, ανθρωπομορφικούς ρομποτικούς βραχίονες μέσω ασύρματης εμπορικής ΔΥΕ, χρησιμοποιώντας κιναισθητική νοερή κίνηση για την εκτέλεση διαφορετικών κινήσεων των άνω άκρων που αντιστοιχούσαν στις τάξεις των ΟΝΚ. Αυτές οι κατηγορίες ομαδοποιήθηκαν σε κατηγορίες, που χρησιμοποιήθηκαν στην ανάλυση τόσο των δικτύων όσο και της χρήσης ΔΥΕ. Η μετρική Directed Transfer Function χρησιμοποιήθηκε για να μελετήσει τη φλοιική λειτουργική συνδεσιμότητα μεταξύ των κόμβων του αισθητικοκινητικού δικτύου. Η μελέτη των λειτουργικών δικτύων πραγματοποιήθηκε μέσω της θεωρίας γράφων και στατιστικών μεθόδων ανάλυσης δικτύων. Επίσης, η εκπαίδευση και η επίδοση στη ΔΥΕ αναλύθηκαν σε σχέση με τα δημογραφικά στοιχεία, τη νευρολογική κατάσταση, την λειτουργική ανεξαρτησία, τη ζωηρότητα της νοερής απεικόνισης, την ψυχομετρική αξιολόγηση και την αντίληψη των χρηστών. Ένα υποσύνολο του αισθητοκινητικού δικτύου που περιλαμβάνει τις πρωτογενείς κινητικές και σωματοαισθητικές περιοχές έδειξε σημαντικά μειωμένη ισχύ συνδεσιμότητας στην ΚΝΜ, ανεξάρτητα από θετική ή αρνητική νευρολογική έκβαση. Οι κινητικές περιοχές της έλικας του μεσολοβίου συνέβαλαν σταθερά στη μεγαλύτερη εκροή (δεξιά) και έλαβαν την υψηλότερη εισροή (αριστερά) πληροφορίας μεταξύ όλων των κόμβων σε όλες τις κατηγορίες νοερής κίνησης και στις δύο ομάδες. Τα δίκτυα του α ρυθμού ήταν λιγότερο πυκνά, παρουσιάζοντας μειωμένη ενσωμάτωση και αυξημένο διαχωρισμό από τα δίκτυα του β ρυθμού. Οι ασθενείς παρουσίασαν σημεία αυξημένης τοπικής επεξεργασίας ως προσαρμοστικό μηχανισμό. Οι υγιείς, η υποομάδα ασθενών με θετική έκβαση και η υποομάδα με τραυματισμούς του αυχένα απέδωσαν καλύτερα στον έλεγχο ΔΥΕ. Η αντίληψη του χρήστη για το ρομπότ δεν διέφερε μεταξύ των ομάδων. Η υποομάδα ΚΝΜ με αρνητική έκβαση βαθμολόγησε υψηλότερα τα ρομποτικά άκρα στον Ανθρωπομορφισμό. Τα δίκτυα ασθενών και υγιών ατόμων μοιράζονται παρόμοια πρότυπα, αλλά νέες λειτουργικές αλληλεπιδράσεις εντοπίζονται ως μοναδικές στη ΚΝΜ. Η χρόνια διαταραχή της αμοιβαίας επικοινωνίας σε ατελή ΚΝΜ μπορεί να οδηγήσει σε μόνιμη σημαντική αρνητική επίδραση επί της συνδεσιμότητας του αισθητικοκινητικού δικτύου και στην εμφάνιση προσαρμοστικών μηχανισμών νευρικής πλαστικότητας

Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization. Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system. Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup. Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments

Detection of Eye Movements based on EEG Signals and the SAX algorithm

[[abstract]]For patients with disabilities, particularly those with motor disabilities and difficulties to interact with computer and devices, Human-Machine Interaction (HMI) research may provide them new ways to solve this problem. In this paper, we propose the Brain-Computer Interface (BCI) approach as a potential technique. The patients may use a portable electroencephalography (EEG) device to give instruction to a computing device via eye movements. Classification algorithms have been investigated in past research to allow detection of eye movement. We would like to investigate another technique, namely the Symbolic Aggregate Approximation (SAX) algorithm, to find out its suitability and performance against known classification algorithms such as Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Decision Tree (DT).[[notice]]補正完