Brain-computer Interface Protocols Research Articles

Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.

Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.

Measuring neuronal avalanches to inform brain-computer interfaces

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control

Brain–computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements’ neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8–13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user’s training should take into account individual differences in hand dominance.

Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity with A Self-Paced Brain-Computer Interface

Brain-computer interfaces (BCIs) can be used to induce neural plasticity in the human nervous system by pairing motor cortical activity with relevant afferent feedback, which can be used in neurorehabilitation. The aim of this study was to identify the optimal type or combination of afferent feedback modalities to increase cortical excitability in a BCI training intervention. In three experimental sessions, 12 healthy participants imagined a dorsiflexion that was decoded by a BCI which activated relevant afferent feedback: (1) electrical nerve stimulation (ES) (peroneal nerve—innervating tibialis anterior), (2) passive movement (PM) of the ankle joint, or (3) combined electrical stimulation and passive movement (Comb). The cortical excitability was assessed with transcranial magnetic stimulation determining motor evoked potentials (MEPs) in tibialis anterior before, immediately after and 30 min after the BCI training. Linear mixed regression models were used to assess the changes in MEPs. The three interventions led to a significant (p < 0.05) increase in MEP amplitudes immediately and 30 min after the training. The effect sizes of Comb paradigm were larger than ES and PM, although, these differences were not statistically significant (p > 0.05). These results indicate that the timing of movement imagery and afferent feedback is the main determinant of induced cortical plasticity whereas the specific type of feedback has a moderate impact. These findings can be important for the translation of such a BCI protocol to the clinical practice where by combining the BCI with the already available equipment cortical plasticity can be effectively induced. The findings in the current study need to be validated in stroke populations.

Classification of auditory selective attention using spatial coherence and modular attention index

Background and Objective: Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. The latter is a new method using modular attention index. The new classifier was developed to implement an auditory BCI where a volunteer makes binary choices using selective attention under the amplitude-modulated tones stimulation.Methods: Contralateral and modular classifiers were applied to the electroencephalogram (EEG) recorded from 144 subjects under the BCI protocol. The best set of parameters (carriers of the stimulus, channels and trials of signal) for this BCI was investigated taking into consideration the hit rate and the information transfer rate.Results: The best result obtained using the modular classifier was a hit rate of 91.67% and information transfer rate of 6.74 bits/min using 0.5 kHz/4.0 kHz as stimuli and three windows (5.10 sec of EEG signal). These results were obtained with five electrodes (C3, P3, F8, P4, O2) using exhaustive search to identify regions with greater coherence.Conclusion: The modular classifier - using electroencephalogram channels from the central, frontal, occipital and parietal areas - improves the performance of auditory BCIs based on selective attention.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates.

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.

Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy

Abstract The integration of Brain-Computer-Interfaces (BCI) into rehabilitation research is a promising approach that may substantially impact the rehabilitation success. Yet, there is still significant challenges that needs to be addressed before the BCI technology can be fully used effectively in a clinical setting as a neural prosthesis for motor impaired users. As it is still unknown whether the conventional BCI induction strategies that use different the types of stimuli and/or mental tasks induce cortical reorganization for disabled users. This paper presents a design and evaluation of a real-time Near-Infrared Spectroscopy (NIRS) based BCI protocol to control an external haptic device, and an interesting source of brain signals that may convey complementary information for inducing neuroplasticity. The protocol is based on the ideas derived from Mirror-based Therapy (MT) in which subjects not only perform literal motor imagery tasks but also combine their intents with visual action observation of a related motor imagery task. The NIRS-BCI system then commands a haptic device in real-time to move in opposing directions of leftward and rightward movement. We also compare the proposed protocol to the conventional limb motor imagery task and verify its efficacy with online decoding accuracies up to 94.99%. The initial validation of the experimental setup was done with seven healthy subjects. Nonetheless we contend that the design of the current NIRS-BCI method hold promise with patient populations for effective stroke rehabilitation therapy, because the beneficial effects of MT alone in post-stroke recovery has already been manifested in the literature.

Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16min-1, which outperform current literature findings.

Remote Navigation of Turtle by Controlling Instinct Behavior via Human Brain-computer Interface

Brain-Computer Interface (BCI) techniques have advanced to a level where it is now eliminating the need for hand-based activation. This paper presents a novel attempt to remotely control an animal's behavior by human BCI using a hybrid of Event Related Desynchronization (ERD) and Steady-State Visually Evoked Potential (SSVEP) BCI protocols. The turtle was chosen as the target animal, and we developed a head-mounted display, wireless communication, and a specially designed stimulation device for the turtle. These devices could evoke the turtle's instinctive escape behavior to guide its moving path, and turtles were remotely controlled in both indoor and outdoor environments. The system architecture and design were presented. To demonstrate the feasibility of the system, experimental tests were performed under various conditions. Our system could act as a framework for future human-animal interaction systems.

Interaction of BCI with the underlying neurological conditions in patients: pros and cons.

Interaction of BCI with the underlying neurological conditions in patients: pros and cons.

A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study

The current study investigated the effects of psychological well-being measured as quality of life (QoL), depression, current mood and motivation on brain–computer interface (BCI) performance in amyotrophic lateral sclerosis (ALS). Six participants with most advanced ALS were trained either for a block of 20 sessions with a BCI based on sensorimotor rhythms (SMR) or a block of 10 sessions with a BCI based on event-related potentials, or both. Questionnaires assessed QoL and severity of depressive symptoms before each training block and mood and motivation before each training session. The SMR-BCI required more training than the P300-BCI. The information transfer rate was higher with the P300-BCI (3.25 bits/min) than with the SMR-BCI (1.16 bits/min). Mood and motivation were related to the number of BCI sessions. Motivational factors, specifically challenge and mastery confidence, were positively related to BCI performance (controlled for the number of sessions) in tow participants, while incompetence fear was negatively related with performance in one participant. BCI performance was not related to motivational factors in three other participants nor to mood in any of the six participants. We conclude that motivational factors may be related to BCI performance in individual subjects and suggest that motivational factors and well-being should be assessed in standard BCI protocols. We also recommend using P300-based BCI as first choice in severely paralyzed patients who present with a P300 evoked potential.