EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient

The present study reports on the use of an EEG-based asynchronous (uncued, user-driven) brain-computer interface (BCI) for the control of functional electrical stimulation (FES). By the application of FES, noninvasive restoration of hand grasp function in a tetraplegic patient was achieved. The patient was able to induce bursts of beta oscillations by imagination of foot movement. These beta oscillations were recorded in a one EEG-channel configuration, bandpass filtered and squared. When this beta activity exceeded a predefined threshold, a trigger for the FES was generated. Whenever the trigger was detected, a subsequent switching of a grasp sequence composed of 4 phases occurred. The patient was able to grasp a glass with the paralyzed hand completely on his own without additional help or other technical aids

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system

Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy

Detecting Awareness in the Vegetative State: Electroencephalographic Evidence for Attempted Movements to Command

Patients in the Vegetative State (VS) do not produce overt motor behavior to command and are therefore considered to be unaware of themselves and of their environments. However, we recently showed that high-density electroencephalography (EEG) can be used to detect covert command-following in some VS patients. Due to its portability and inexpensiveness, EEG assessments of awareness have the potential to contribute to a standard clinical protocol, thus improving diagnostic accuracy. However, this technique requires refinement and optimization if it is to be used widely as a clinical tool. We asked a patient who had been repeatedly diagnosed as VS for 12-years to try to move his left and right hands, between periods of rest, while EEG was recorded from four scalp electrodes. We identified appropriate and statistically reliable modulations of sensorimotor beta rhythms following commands to try to move, which could be significantly classified at a single-trial level. These reliable effects indicate that the patient attempted to follow the commands, and was therefore aware, but was unable to execute an overtly discernable action. The cognitive demands of this novel task are lower than those used previously and, crucially, allow for awareness to be determined on the basis of a 20-minute EEG recording made with only four electrodes. This approach makes EEG assessments of awareness clinically viable, and therefore has potential for inclusion in a standard assessment of awareness in the VS

Starling: A Blockchain-based System for Coordinated Obstacle Mapping in Dynamic Vehicular Environments

Current Vehicle-to-Vehicle solutions cannot ensure the authenticity of safety-critical vehicle and traffic data. Moreover, they do not allow malicious vehicles to be detected and eliminated. However, this is becoming mandatory, as more and more vehicles are on the road and communicating with each other. We propose a system called Starling, which focuses on trusted coordinated obstacle mapping using blockchain technology and a distributed database. Starling enables vehicles to share detected obstacles with other vehicles in a secure and verifiable manner, thus improving road safety. It ensures that data was not manipulated, changed, or deleted and is based on an open protocol so that vehicles can exchange data regardless of their manufacturer. In a case study, we demonstrate how a consensus is reached among vehicles and conduct a comprehensive evaluation of the Starling system using Ethereum and the InterPlanetary File System

Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study

Background and Purpose: Impaired hand function decreases quality of life in persons with tetraplegia. We tested functional electrical stimulation (FES) controlled by a hybrid brain-computer interface (BCI) for improving hand function in participants with tetraplegia. Methods: Two participants with subacute tetraplegia (participant 1: C5 Brown-Sequard syndrome, participant 2: complete C5 lesion) took part in this proof-of-concept study. The goal was to determine whether the BCI system could drive the FES device by accurately classifying participants' intent (open or close the hand). Participants 1 and 2 received 10 sessions and 4 sessions of BCI-FES, respectively. A novel time-switch BCI strategy based on motor imagery was used to activate the FES. In one session, we tested a hybrid BCI-FES based on 2 spontaneously generated brain rhythms: a sensory-motor rhythm during motor imagery to activate a stimulator and occipital alpha rhythms to deactivate the stimulator. Participants received BCI-FES therapy 2 to 3 times a week in addition to conventional therapy. Imagery ability and muscle strength were measured before and after treatment. Results: Visual feedback was associated with a 4-fold increase of brain response during motor imagery in both participants. For participant 1, classification accuracy (open/closed) for motor imagery-based BCI was 83.5% (left hand) and 83.8% (right hand); participant 2 had a classification accuracy of 83.8% for the right hand. Participant 1 had moderate improvement in muscle strength, while there was no change for participant 2. Discussion and Conclusion: We demonstrated feasibility of BCI-FES, using 2 naturally generated brain rhythms. Studies on a larger number of participants are needed to separate the effects of BCI training from effects of conventional therapy