Serafeim Perdikis

The role of shared-control in BCI-based telepresence

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact actively with relatives and friends located in different rooms. However, the control of robots through an uncertain channel as a BCI may be complicated and exhaustive. Shared control can facilitate the operation of brain-controlled telepresence robots, as demonstrated by the experimental results reported here. In fact, it allows all subjects to complete a rather complex task, driving the robot in a natural environment along a path with several targets and obstacles, in shorter times and with less number of mental commands.

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

OBJECTIVE While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. APPROACH This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. MAIN RESULTS We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. SIGNIFICANCE This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

The effect of multimodal and enriched feedback on SMR-BCI performance

OBJECTIVE This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel (funnel feedback) provided enriched visual or enriched multimodal feedback. METHODS In a between subject design 30 healthy SMR-BCI naive participants were provided with either conventional bar feedback (CB), or visual funnel feedback (UF), or multimodal (visual and auditory) funnel feedback (MF). Subjects were required to imagine left and right hand movement and were trained to control the SMR based BCI for five sessions on separate days. RESULTS Feedback accuracy varied largely between participants. The MF feedback lead to a significantly better performance in session 1 as compared to the CB feedback and could significantly enhance motivation and minimize frustration in BCI use across the five training sessions. CONCLUSION The present study demonstrates that the BCI funnel feedback allows participants to modulate sensorimotor EEG rhythms. Participants were able to control the BCI with the funnel feedback with better performance during the initial session and less frustration compared to the CB feedback. SIGNIFICANCE The multimodal funnel feedback provides an alternative to the conventional cursorbar feedback for training subjects to modulate their sensorimotor rhythms.

A hybrid BCI for enhanced control of a telepresence robot

Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work, we propose to exploit the users residual muscular activity to provide a fast and reliable control channel, which can start/stop the telepresence robot at any moment. Our preliminary results show that not only does this hybrid approach increase the accuracy, but it also helps to reduce the workload and was the preferred control paradigm of all the participants.

Context-aware adaptive spelling in motor imagery BCI

OBJECTIVE This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subjects performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. APPROACH Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTrees language model to improve online expectation-maximization maximum-likelihood estimation. MAIN RESULTS Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. SIGNIFICANCE We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human–machine interfaces is a promising avenue for the restoration and substitution of lost and impaired functions for these users. In this study, we present a novel hand exoskeleton, mano, designed to assist and restore hand functions of people with motor disabilities during activities of daily living (ADL) and in neurorehabilitative scenarios. Compared to state-of-the-art devices, our system is fully wearable, portable, and minimally obtrusive on the hand. The exoskeleton can actively control flexion and extension of all fingers, while allowing natural somatosensorial interactions with the environment surrounding the users. We evaluated the device from four different perspectives. A mechanical characterization, showing that the exoskeleton can cover more than 70% of healthy hand workspace and it can achieve forces at the fingertips sufficient for ADL. A functional characterization, where we showed how two users who suffered from spinal cord injuries were able to perform several ADL for the first time since their accidents. Third, we evaluated the system from a neuroimaging perspective, showing that the device can elicit EEG brain patterns typical of natural hand motions. We finally exemplified the control of the hand exoskeleton within an exemplar framework, a brain–machine interface scenario, showing that motor intention can be decoded for a continuous control of the device. Overall, our results showed that the device represents an ecological solution for use both in ADL and in scenarios aimed at promoting sensorimotor recovery.

Subject-oriented training for motor imagery brain-computer interfaces

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec.

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.Brain-computer interface (BCI) can improve motor skills on stroke patients. This study shows that BCI-controlled neuromuscular electrical stimulation therapy can cause cortical reorganization due to activation of efferent and afferent pathways, and this effect can be long lasting in a brain region specific manner.

Brain Racers: How paralysed athletes used a Brain-Computer Interface to win gold at the cyborg olympics

IN OCTOBER 2016, inside a sold-out arena in Zurich, a man named Numa Poujouly steered his wheelchair up to the central podium. As the Swiss national anthem played, organizers of the worlds first cyborg Olympics hung a gold medal around Poujoulys neck. The 30-yearold, who became paralyzed after a bicycle accident in his teens, had triumphed in the tournaments most futuristic event: a video-game-like race in which the competitors controlled their speeding avatars with just their minds.

Quantifying Electrode Reliability During Brain–Computer Interface Operation

One of the problems of noninvasive brain-computer interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operators attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can for instance be the result of electrode misplacement, degrading impedance or loss of connectivity. Since this problem can develop at run time, there is a need of a systematic approach to evaluate electrode reliability during online BCI operation. In this paper, we propose two metrics detecting how much each channel is deviating from its expected behavior. This quantifies electrode reliability at run time which could be embedded into BCI data processing to increase performance. We assess the effectiveness of these metrics in quantifying signal degradation by conducting three experiments: Electrode swap, electrode manipulation, and offline artificially degradation of P300 signals.