Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten individuals without a disability) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

Increasing the independence of individuals with tetraplegia is a challenging task. One potential solution is to allow for use of an assistive robotic manipulator (ARM), when solving tasks in personal and remote space. There is a lack in available control interfaces that are suitable for severely disabled individuals. The aim of this paper is twofold: to allow for remote tongue-based control of an ARM and, to study the effect of semiautomation when compared with full manual control of an ARM. Ten able-bodied individuals participated in a two-day experiment where they were asked to drive a wheelchair mounted ARM away from the participant and out of sight. Thereafter, they should either pick up a strawberry or a bottle from a table. All the participants successfully finished three trials for three different control methods: 1) manual control (MA), 2) adaptive level semiautomation (SA), and 3) fixed level semi-automation (FA). The data was analyzed using repeated measures analysis of variance. When grasping the strawberry, there was a significant decrease in the gripping time (60.16±9.13 vs. 90.62±10.06, p = 0.012) and number of used commands (0.73±0.07 vs. 0.86±0.09, p = 0.03) when using FA compared to MA. When grasping the bottle, the SA showed a significant reduction in gripping time (32.30±3.09 vs. 66.15±9.77, p = 0.022) and number of used commands (0.63±0.05 vs. 0.85±0.09, p<0.001) compared with MA. This paper is a step in the direction of providing severely paralyzed individuals with a way to increase their independence and overall quality of life

Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.

Hand exoskeletons and functional electrical stimulation (FES) have shown promise in restoring hand function after stroke or cervical spinal cord injury. However, major challenges remain in providing reliable user command input in individuals with severe paralysis and in enabling safe and effective control of hand closing and opening. Here, we present a non-invasive tongue-based control strategy for a hybrid soft hand exoskeleton system combining tongue-controlled independent tendon-driven flexion of five fingers with tongue-triggered FES-induced finger extension. This user-intent-driven hybrid approach supports multiple activities of daily living, such as grasping a bottle or toothbrush, and enables hand opening while mitigating muscle fatigue and reducing system complexity. The system was evaluated in five users with cervical spinal cord injury, demonstrating high task success rates in daily living tasks and user preference due to its wearability and ease of use, as well as consistently low reported levels of discomfort, pain, and fatigue. These findings show that the proposed hybrid system can restore activities of daily living in individuals with cervical tetraplegia, including those with long-term injuries.