Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems.

OBJECTIVE: This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. APPROACH: To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. MAIN RESULTS: Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. SIGNIFICANCE: Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations.

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.

OBJECTIVE: For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. APPROACH: Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture. MAIN RESULTS: In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold. SIGNIFICANCE: The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot.

Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

Developing Non-Somatotopic Phantom Finger Sensation to Comparable Levels of Somatotopic Sensation through User Training With Electrotactile Stimulation.

Cutaneous electrical stimulation can provide tactile feedback for upper-limb amputees through somatotopic feedback (SF) or non-somatotopic feedback (NF). The SF delivers electrotactile stimulus to projection finger maps (PFMs) on the stumps of amputees, which outperforms NF that transfers stimulus to other human intact skin areas in general. However, the SF areas on stumps are very limited and often occupied by electromyography (EMG) sensors in application of myoelectric prosthesis. This work aims at improving NF performance on human upper arms through user training with electrotactile stimulation. The experiments were conducted over seven consecutive days on nine able-bodied subjects and two forearm amputees. The performance measures of NF/SF included the correct identification rates (CIRs), the response time and the NASA-TLX questionnaire. The between-day CIR s on NF sites increased logarithmically with a mean course of 3-day rapid-improving phase and plateaued in the relative-steady phase. The response time and NASA-TLX scores could also rapidly reduce to the comparable levels of the SF areas during the same mean period of 3-day rapid-improving phase, respectively. These results indicated that the performance of NF could be highly improved to the equivalent level as that of SF through 3-day electrotactile training, which we named as "3-day effect". It provides important insights that intact skin areas without phantom sensations can effectively replace SF sites to transfer tactile feedback after continuous user training, which validates effectiveness of non-invasive interfaces of tactile feedback for upper-limb amputees in practice.

Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.

Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single- and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in a way that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal systems or anthropomorphic robotic arms, may benefit from allowing neural control over redundant configuration dimensions as well as the end-effector.

A P300-Based Threshold-Free Brain Switch and Its Application in Wheelchair Control.

The key issue of electroencephalography (EEG)-based brain switches is to detect the control and idle states in an asynchronous manner. Most existing methods rely on a threshold. However, it is often time consuming to select a satisfactory threshold, and the chosen threshold might be inappropriate over a long period of time due to the variability of the EEG signals. This paper presents a new P300-based threshold-free brain switch. Specifically, one target button and three pseudo buttons, which are intensified in a random order to produce P300 potential, are set in the graphical user interface. The user can issue a switch command by focusing on the target button. Two support vector machine (SVM) classifiers, namely, SVM1 and SVM2, are used in the detection algorithm. During detection, we first obtained four SVM scores, corresponding to the four flashing buttons, by applying SVM1 to the ongoing EEG. If the SVM score corresponding to the target button was negative or not at the maximum, then an idle state was determined. Moreover, if the target button had a maximum and positive score, then we fed the four SVM scores as features into SVM2 to further discriminate the control and idle states. As an application, this brain switch was used to produce a start/stop command for an intelligent wheelchair, of which the left, right, forward, backward functions were carried out by an autonomous navigation system. Several experiments were conducted with eight healthy subjects and five patients with spinal cord injuries (SCIs). The experimental results not only demonstrated the effectiveness of our approach but also illustrated the potential application for patients with SCIs.

Association of Individual Characteristics with Teleoperation Performance.

BACKGROUND: A number of space activities (e.g., extravehicular astronaut rescue, cooperation in satellite services, space station supplies, and assembly) are implemented directly or assisted by remote robotic arms. Our study aimed to reveal those individual characteristics which could positively influence or even predict teleoperation performance of such a space robotic arm. METHODS: There were 64 male volunteers without robot operation experience recruited for the study. Their individual characteristics were assessed, including spatial cognitive ability, cognitive style, and personality traits. The experimental tasks were three abstracted teleoperation tasks of a simulated space robotic arm: point aiming, line alignment, and obstacle avoidance. Teleoperation performance was measured from two aspects: task performance (completion time, extra distance moved, operation slips) and safety performance (collisions, joint limitations reached). The Pearson coefficients between individual characteristics and teleoperation performance were examined along with performance prediction models. RESULTS: It was found that the subjects with relatively high mental rotation ability or low neuroticism had both better task and safety performance (|r| = 0.212 ∼ 0.381). Subjects with relatively high perspective taking ability or high agreeableness had better task performance (r = -0.253; r = -0.249). Imagery subjects performed better than verbal subjects regarding both task and safety performance (|r| = 0.236 ∼ 0.290). Compared with analytic subjects, wholist subjects had better safety performance (r = 0.300). Additionally, extraverted subjects had better task performance (r = -0.259), but worse safety performance (r = 0.230). CONCLUSIONS: Those with high spatial cognitive ability, imagery and wholist cognitive style, low neuroticism, and high agreeableness were seen to have more advantages in working with the remote robotic arm. These results could be helpful to astronaut selection and training for space station missions. Pan D, Zhang Y, Li Z, Tian Z. Association of individual characteristics with teleoperation performance. Aerosp Med Hum Perform. 2016; 87(9):772-780.

Development of a Regenerative Peripheral Nerve Interface for Control of a Neuroprosthetic Limb.

Background. The purpose of this experiment was to develop a peripheral nerve interface using cultured myoblasts within a scaffold to provide a biologically stable interface while providing signal amplification for neuroprosthetic control and preventing neuroma formation. Methods. A Regenerative Peripheral Nerve Interface (RPNI) composed of a scaffold and cultured myoblasts was implanted on the end of a divided peroneal nerve in rats (n = 25). The scaffold material consisted of either silicone mesh, acellular muscle, or acellular muscle with chemically polymerized poly(3,4-ethylenedioxythiophene) conductive polymer. Average implantation time was 93 days. Electrophysiological tests were performed at endpoint to determine RPNI viability and ability to transduce neural signals. Tissue samples were examined using both light microscopy and immunohistochemistry. Results. All implanted RPNIs, regardless of scaffold type, remained viable and displayed robust vascularity. Electromyographic activity and stimulated compound muscle action potentials were successfully recorded from all RPNIs. Physiologic efferent motor action potentials were detected from RPNIs in response to sensory foot stimulation. Histology and transmission electron microscopy revealed mature muscle fibers, axonal regeneration without neuroma formation, neovascularization, and synaptogenesis. Desmin staining confirmed the preservation and maturation of myoblasts within the RPNIs. Conclusions. RPNI demonstrates significant myoblast maturation, innervation, and vascularization without neuroma formation.

MIT-Skywalker: A Novel Gait Neurorehabilitation Robot for Stroke and Cerebral Palsy.

The MIT-Skywalker is a novel robotic device developed for the rehabilitation or habilitation of gait and balance after a neurological injury. It represents an embodiment of the concept exhibited by passive walkers for rehabilitation training. Its novelty extends beyond the passive walker quintessence to the unparalleled versatility among lower extremity devices. For example, it affords the potential to implement a novel training approach built upon our working model of movement primitives based on submovements, oscillations, and mechanical impedances. This translates into three distinct training modes: discrete, rhythmic, and balance. The system offers freedom of motion that forces self-directed movement for each of the three modes. This paper will present the technical details of the robotic system as well as a feasibility study done with one adult with stroke and two adults with cerebral palsy. Results of the one-month feasibility study demonstrated that the device is safe and suggested the potential advantages of the three modular training modes that can be added or subtracted to tailor therapy to a particular patient's need. Each participant demonstrated improvement in common clinical and kinematic measurements that must be confirmed in larger randomized control clinical trials.

Implementation of EMG- and Force-Based Control Interfaces in Active Elbow Supports for Men With Duchenne Muscular Dystrophy: A Feasibility Study.

While there is an extensive number of studies on the development and evaluation of electromyography (EMG)- and force-based control interfaces for assistive devices, no studies have focused on testing these control strategies for the specific case of adults with Duchenne muscular dystrophy (DMD). This paper presents a feasibility study on the use of EMG and force as control interfaces for the operation of active arm supports for men with DMD. We have built an experimental active elbow support, with a threefold objective: 1) to investigate whether adult men with DMD could use EMG- and force-based control interfaces; 2) to evaluate their performance during a discrete position-tracking task; and 3) to examine users' acceptance of the control methods. The system was tested in three adults with DMD (21-22 years). Although none of the three participants had performed any voluntary movements with their arms for the past 3-5 years, all of them were 100% successful in performing the series of tracking tasks using both control interfaces (mean task completion time EMG: [Formula: see text] , force: [Formula: see text] ). While movements with the force-based control were considerably smoother in Subject 3 and faster in Subject 1, EMG based-control was perceived as less fatiguing by all three subjects. Both EMG- and force-based interfaces are feasible solutions for the control of active elbow supports in adults with DMD and should be considered for further investigations on multi-DOF control.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm.

Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.

An Approach for the Cooperative Control of FES With a Powered Exoskeleton During Level Walking for Persons With Paraplegia.

This paper describes a hybrid system that combines a powered lower limb exoskeleton with functional electrical stimulation (FES) for gait restoration in persons with paraplegia. The general control structure consists of two control loops: a motor control loop, which utilizes joint angle feedback control to control the output of the joint motor to track the desired joint trajectories, and a muscle control loop, which utilizes joint torque profiles from previous steps to shape the muscle stimulation profile for the subsequent step in order to minimize the motor torque contribution required for joint angle trajectory tracking. The implementation described here incorporates stimulation of the hamstrings and quadriceps muscles, such that the hip joints are actuated by the combination of hip motors and the hamstrings, and the knee joints are actuated by the combination of knee motors and the quadriceps. In order to demonstrate efficacy, the control approach was implemented on three paraplegic subjects with motor complete spinal cord injuries ranging from levels T6 to T10. Experimental data indicates that the cooperative control system provided consistent and repeatable gait motions and reduced the torque and power output required from the hip and knee motors of the exoskeleton compared to walking without FES.

Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation.

The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.

The effects of spectral and temporal parameters on perceived confirmation of an auditory non-speech signal.

In human-machine interactions, the confirmation of an action or input is a very important information for users. A paired comparison experiment explored the effects of four acoustic parameters on the perceived confirmation of auditory non-speech signals. Reducing the frequency-ratio and the pulse-to-pulse time between two successive pulses increased perceived confirmation. The effects of the parameters frequency and number of pulses were not clear-cut. The results provide information for designing auditory confirmation signals. It is shown that findings about the effects of certain parameters on the perceived urgency of warning signals cannot be easily inverted to perceived confirmation.

A novel robotic platform for laser-assisted transurethral surgery of the prostate.

Benign prostatic hyperplasia (BPH) is the most common pathology afflicting ageing men. The gold standard for the surgical treatment of BPH is transurethral resection of the prostate. The laser-assisted transurethral surgical treatment of BPH is recently emerging as a valid clinical alternative. Despite this, there are still some issues that hinder the outcome of laser surgery, e.g., distal dexterity is strongly reduced by the current endoscopic instrumentation and contact between laser and prostatic tissue cannot be monitored and optimized. This paper presents a novel robotic platform for laser-assisted transurethral surgery of BPH. The system, designed to be compatible with the traditional endoscopic instrumentation, is composed of a catheter-like robot provided with a fiber optic-based sensing system and a cable-driven actuation mechanism. The sensing system allows contact monitoring between the laser and the hypertrophic tissue. The actuation mechanism allows steering of the laser fiber inside the prostatic urethra of the patient, when contact must be reached. The design of the proposed robotic platform along with its preliminary testing and evaluation is presented in this paper. The actuation mechanism is tested in in vitro experiments to prove laser steering performances according to the clinical requirements. The sensing system is calibrated in experiments aimed to evaluate the capability of discriminating the contact forces, between the laser tip and the prostatic tissue, from the pulling forces exerted on the cables, during laser steering. These results have been validated demonstrating the robot's capability of detecting sub-Newton contact forces even in combination with actuation.

An independent SSVEP-based brain-computer interface in locked-in syndrome.

OBJECTIVE: Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). APPROACH: Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based 'interlaced squares' stimulation pattern. MAIN RESULTS: Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. SIGNIFICANCE: We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

Towards a holistic assessment of the user experience with hybrid BCIs.

OBJECTIVE: In recent years, brain-computer interfaces (BCIs) have become mature enough to immensely benefit from the expertise and tools established in the field of human-computer interaction (HCI). One of the core objectives in HCI research is the design of systems that provide a pleasurable user experience (UX). While the majority of BCI studies exclusively evaluate common efficiency measures such as classification accuracy and speed, single research groups have begun to look at further usability aspects such as ease of use, workload and learnability. However, these evaluation metrics only cover pragmatic aspects of UX while still not considering the hedonic quality of UX. In order to gain a holistic perspective on UX, hedonic quality aspects such as motivation and frustration were also taken into account for our evaluation of three BCI-driven interfaces, which were proposed to be used as a two-stage neuroprosthetic control within the EU project MUNDUS. APPROACH: At the first stage, one of six possible actions was selected and either confirmed or cancelled at the second stage. For the experiment, a solely event-related-potential-based interface (ERP-ERP) and two hybrid solutions were tested that were controlled by ERP and motor imagery (MI)--resulting in the two possible combinations: ERP selection/MI confirmation (ERP-MI) or MI selection/ERP confirmation (MI-ERP). Behavioural, subjective and encephalographic (EEG) data of 12 healthy subjects were collected during an online experiment with the three graphical user interfaces (GUIs). MAIN RESULTS: Results showed a significantly greater pragmatic quality (in terms of accuracy, efficiency, workload, use quality and learnability) for the ERP-ERP and ERP-MI GUIs in contrast to the MI-ERP GUI. Consequently, the MI-ERP GUI is least suited for use as a neuroprosthetic control. With respect to the comparison of the ERP-ERP and ERP-MI GUIs, no significant differences in pragmatic and hedonic quality of UX were found. Since throughout better results were obtained for the conventional approach and it was most preferred by the subjects, the ERP-ERP GUI seems more suitable for its deployment in actual end-users. Nevertheless, for individuals with stable MI patterns, the hybrid interface can be provided as an additional option of choice within the MUNDUS framework. SIGNIFICANCE: Although the paramount goal in BCI research still remains the improvement of classification accuracy and communication speed, it is of significance to note that it is equally important for end-users to keep up their motivation and prevent frustration. By including pragmatic as well as hedonic quality aspects, this study is the first effort to gain a holistic perspective of the UX while interacting with BCI-driven assistive technology aimed at actual end-users. The broad-scale methodology provided valuable insights into the underlying dynamics causing the users' experience to differ across the GUIs. The results will be used to refine a BCI-driven neuroprosthesis and test it with end-users.

Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom.

There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi-degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real-world.