A simulation environment for studying transcutaneous electrotactile stimulation.

Transcutaneous electrical nerve stimulation (TENS) allows the artificial excitation of nerve fibres by applying electric-current pulses through electrodes on the skin's surface. This work involves the development of a simulation environment that can be used for studying transcutaneous electrotactile stimulation and its dependence on electrode layout and excitation patterns. Using an eight-electrode array implementation, it is shown how nerves located at different depths and with different orientations respond to specific injected currents, allowing the replication of already reported experimental findings and the creation of new hypotheses about the tactile sensations associated with certain stimulation patterns. The simulation consists of a finite element model of a human finger used to calculate the distribution of the electric potential in the finger tissues neglecting capacitive effects, and a cable model to calculate the excitation/inhibition of action potentials in each nerve.

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.