BACKGROUND AND OBJECTIVE:Laryngeal dystonia (LD) is a focal task-specific dystonia, affecting speaking but not whispering or emotional vocalizations. Therapeutic options for LD are limited. We developed and tested a non-invasive, closed-loop, neurofeedback, brain-computer interface (BCI) intervention for LD treatment. METHODS:Ten patients with isolated focal LD participated in the study. The personalized BCI system included visual neurofeedback of individual real-time electroencephalographic (EEG) activity during symptomatic speaking compared to asymptomatic whispering, presented in the virtual reality (VR) environment of real-life scenarios. During five consecutive days of intervention, patients used the BCI to learn to modulate their abnormally increased brain activity during speaking and match it to near-normal activity of asymptomatic whispering. Changes in voice symptoms and EEG activity were quantified for the evaluation of BCI effects. RESULTS:Compared to baseline, LD patients had a statistically significant reduction of their voice symptoms on Days 1-5 of BCI intervention. Thi was paralleled by improved controllability of the visual neurofeedback and a significant reduction of left frontal delta power, including superior and middle frontal gyri, on Day 1 and left central gamma power, including premotor, primary sensorimotor, and inferior parietal areas, on Days 3 and 5. The majority of patients (70%) reported sustained positive effects of the BCI intervention on their voice quality 1 week after the study participation. CONCLUSION:The closed-loop BCI neurofeedback intervention specifically targeting disorder pathophysiology shows significant potential as a novel treatment option for patients with LD and likely other forms of task-specific focal dystonia. © 2025 International Parkinson and Movement Disorder Society.

Music is a powerful medium for influencing listeners' emotional states, and this capacity has driven a surge of research interest in AI-based affective music generation in recent years. Many existing systems, however, are a black box which are not directly controllable, thus making these systems less flexible and adaptive to users. We present AffectMachine-Pop, an expert system capable of generating retro-pop music according to arousal and valence values, which can either be pre-determined or based on a listener's real-time emotion states. To validate the efficacy of the system, we conducted a listening study demonstrating that AffectMachine-Pop is capable of generating affective music at target levels of arousal and valence. The system is tailored for use either as a tool for generating interactive affective music based on user input, or for incorporation into biofeedback or neurofeedback systems to assist users with emotion self-regulation.

Background: Mismatch negativity (MMN), an event-related potential (ERP) component occurring at specific recording sites and latency, is associated with an automatic change detection response, generally elicited using oddball paradigms wherein infrequent stimuli are embedded in repeated, frequent stimuli. To verify the presence of mismatch-related ERP responses to somatosensory stimulation in individuals with cerebral palsy (CP), we conducted a preliminary study involving healthy participants and patients with CP. Methods: Both groups underwent ‘frequent’ and ’infrequent’ stimulation applied to the ring finger and thumb of their left hand, respectively. ERPs were recorded at frontal, central, and parietal scalp locations using electroencephalography. A healthy cohort tested the experimental protocol and showed evidence that mismatch-related ERP responses were observable. Subsequent analysis focused on the patient group. Results: Statistically significant differences between the two types of stimuli were observed on the frontocentral and parietal channels between 150 and 250 ms after the stimulus onset in the patient group. Furthermore, a late discriminative response was observed in the frontal and parietal channels. Conclusion: The results demonstrate the presence of mismatch-related ERP responses in individuals with CP.

BACKGROUND:Laryngeal dystonia (LD) is an isolated focal dystonia characterized by involuntary spasms in laryngeal muscles selectively impairing speech production. Anecdotal observations reported the worsening of LD symptoms in stressful or vocally demanding situations. OBJECTIVES:To examine the impact of surrounding audio-visual complexity on LD symptomatology for a better understanding of disorder phenomenology. METHODS:We developed well-controlled virtual reality (VR) environments of real-life interpersonal communications to investigate how different levels of audio-visual complexity may impact LD symptoms. The VR experiments were conducted over five consecutive days, during which each patient experienced 10 h of 4100 experimental trials in VR with gradually increasing audio-visual complexity. Daily reports were collected about patients' voice changes, as well as their comfort, engagement, concentration, and drowsiness from using VR technology. RESULTS:After a weekly VR exposure, 82% of patients reported changes in their voice symptoms related to changes in background audio-visual complexity. Significant differences in voice symptoms were found between the first two levels of the audio-visual challenge complexity independent of study sessions or VR environments. CONCLUSION:This study demonstrated that LD symptoms are impacted by audio-visual background across various virtual realistic settings. These findings should be taken into consideration when planning behavioral experiments or evaluating the outcomes of clinical trials in these patients. Moreover, these data show that VR presents a reliable and useful technology for providing real-life assessments of the impact of various experimental settings, such as during the testing of novel therapeutic interventions in these patients. LEVEL OF EVIDENCE:3 Laryngoscope, 135:787-793, 2025.

This study differentiates the dynamic temporal-spectral effects during listening to 10 s of familiar music compared with unfamiliar music. This study highlights that listening to familiar music leads to continuous suppression in the alpha and low-beta bands. This suppression starts ∼800 ms after the stimulus onset.

Background and ObjectiveLaryngeal dystonia (LD) is focal task-specific dystonia, predominantly affecting speech but not whispering or emotional vocalizations. Prior neuroimaging studies identified brain regions forming a dystonic neural network and contributing to LD pathophysiology. However, the underlying temporal dynamics of these alterations and their contribution to the task-specificity of LD remain largely unknown. The objective of the study was to identify the temporal-spatial signature of altered cortical oscillations associated with LD pathophysiology. MethodsWe used high-density 128-electrode electroencephalography (EEG) recordings during symptomatic speaking and two asymptomatic tasks, whispering and writing, in 24 LD patients and 22 healthy individuals to investigate the spectral dynamics, spatial localization, and interregional effective connectivity of aberrant cortical oscillations within the dystonic neural network, as well as their relationship with LD symptomatology. ResultsSymptomatic speaking in LD patients was characterized by significantly increased gamma synchronization in the middle/superior frontal gyri, primary somatosensory cortex, and superior parietal lobule, establishing the altered prefrontal-parietal loop. Hyperfunctional connectivity from the left middle frontal gyrus to the right superior parietal lobule was significantly correlated with the age of onset and the duration of LD symptoms. Asymptomatic whisper in LD patients had not no statistically significant changes in any frequency band, whereas asymptomatic writing was characterized by significantly decreased synchronization of beta-band power localized in the right superior frontal gyrus. ConclusionTask-specific oscillatory activity of prefrontal-parietal circuitry is likely one of the underlying mechanisms of aberrant heteromodal integration of information processing and transfer within the neural network leading to dystonic motor output. & COPY; 2023 International Parkinson and Movement Disorder Society.

Human-robot interaction (HRI) describes scenarios in which both human and robot work as partners, sharing the same environment or complementing each other on a joint task. HRI is characterized by the need for high adaptability and flexibility of robotic systems toward their human interaction partners. One of the major challenges in HRI is task planning with dynamic subtask assignment, which is particularly challenging when subtask choices of the human are not readily accessible by the robot. In the present work, we explore the feasibility of using electroencephalogram (EEG) based neuro-cognitive measures for online robot learning of dynamic subtask assignment. To this end, we demonstrate in an experimental human subject study, featuring a joint HRI task with a UR10 robotic manipulator, the presence of EEG measures indicative of a human partner anticipating a takeover situation from human to robot or vice-versa. The present work further proposes a reinforcement learning based algorithm employing these measures as a neuronal feedback signal from the human to the robot for dynamic learning of subtask-assignment. The efficacy of this algorithm is validated in a simulation-based study. The simulation results reveal that even with relatively low decoding accuracies, successful robot learning of subtask-assignment is feasible, with around 80% choice accuracy among four subtasks within 17 minutes of collaboration. The simulation results further reveal that scalability to more subtasks is feasible and mainly accompanied with longer robot learning times. These findings demonstrate the usability of EEG-based neuro-cognitive measures to mediate the complex and largely unsolved problem of human-robot collaborative task planning.

When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.

Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.

Repeated listening to unknown music leads to gradual familiarization with musical sequences. Passively listening to musical sequences could involve an array of dynamic neural responses in reaching familiarization with the musical excerpts. This study elucidates the dynamic brain response and its variation over time by investigating the electrophysiological changes during the familiarization with initially unknown music. Twenty subjects were asked to familiarize themselves with previously unknown 10 s classical music excerpts over three repetitions while their electroencephalogram was recorded. Dynamic spectral changes in neural oscillations are monitored by time-frequency analyses for all frequency bands (theta: 5-9 Hz, alpha: 9-13 Hz, low-beta: 13-21 Hz, high beta: 21-32 Hz, and gamma: 32-50 Hz). Time-frequency analyses reveal sustained theta event-related desynchronization (ERD) in the frontal-midline and the left pre-frontal electrodes which decreased gradually from 1st to 3rd time repetition of the same excerpts (frontal-midline: 57.90 %, left-prefrontal: 75.93 %). Simi-larly, sustained gamma ERD decreased in the frontal-midline and bilaterally frontal/temporal areas (frontal-midline: 61.47 %, left-frontal: 90.88 %, right-frontal: 87.74 %). During familiarization, the decrease of theta ERD is superior in the first part (1-5 s) whereas the decrease of gamma ERD is superior in the second part (5-9 s) of music excerpts. The results suggest that decreased theta ERD is associated with successfully identifying familiar sequences, whereas decreased gamma ERD is related to forming unfamiliar sequences.

Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.

Soft exosuits offer promise to support users in everyday workload tasks by providing assistance. However, acceptance of such systems remains low due to the difficulty of control compared with rigid mechatronic systems. Recently, there has been progress in developing control schemes for soft exosuits that move in line with user intentions. While initial results have demonstrated sufficient device performance, the assessment of user experience via the cognitive response has yet to be evaluated. To address this, we propose a soft pneumatic elbow exosuit designed based on our previous work to provide assistance in line with user expectations utilizing two existing state-of-the-art control methods consisting of a gravity compensation and myoprocessor based on muscle activation. A user experience study was conducted to assess whether the device moves naturally with user expectations and the potential for device acceptance by determining when the exosuit violated user expectations through the neuro-cognitive and motor response. Brain activity from electroencephalography (EEG) data revealed that subjects elicited error-related potentials (ErrPs) in response to unexpected exosuit actions, which were decodable across both control schemes with an average accuracy of 76.63 ± 1.73% across subjects. Additionally, unexpected exosuit actions were further decoded via the motor response from electromyography (EMG) and kinematic data with a grand average accuracy of 68.73 ± 6.83% and 77.52 ± 3.79% respectively. This work demonstrates the validation of existing state-of-the-art control schemes for soft wearable exosuits through the proposed soft pneumatic elbow exosuit. We demonstrate the feasibility of assessing device performance with respect to the cognitive response through decoding when the device violates user expectations in order to help understand and promote device acceptance.

Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of decoding accuracy, their power consumption is relatively high for mobile applications. Neuromorphic hardware arises as a promising solution to tackle this problem since it can run massive spiking neural networks with energy consumption orders of magnitude lower than traditional hardware. Herein, we show the viability of directly mapping a continuous-valued convolutional neural network for motor imagery EEG classification to a spiking neural network. The converted network, able to run on the SpiNNaker neuromorphic chip, only shows a 1.91% decrease in accuracy after conversion. Thus, we take full advantage of the benefits of both deep learning accuracies and low-power neuro-inspired hardware, properties that are key for the development of wearable BCI devices.

This paper presents a visually-guided grip selection based on the combination of object recognition and tactile feedback of a soft-hand exoskeleton intended for hand rehabilitation. A pre-trained neural network is used to recognize the object in front of the hand exoskeleton, which is then mapped to a suitable grip type. With the object cue, it actively assists users in performing different grip movements without calibration. In a pilot experiment, one healthy user completed four different grasp-and-move tasks repeatedly. All trials were completed within 25 seconds and only one out of 20 trials failed. This shows that automated movement training can be achieved by visual guidance even without biomedical sensors. In particular, in the private setting at home without clinical supervision, it is a powerful tool for repetitive training of daily-living activities.

Exoskeletons and prosthetic devices controlled using brain-computer interfaces (BCIs) can be prone to errors due to inconsistent decoding. In recent years, it has been demonstrated that error-related potentials (ErrPs) can be used as a feedback signal in electroencephalography (EEG) based BCIs. However, modern BCIs often take large setup times and are physically restrictive, making them impractical for everyday use. In this paper, we use a mobile and easy-to-setup EEG device to investigate whether an erroneously functioning 1-DOF exoskeleton in different conditions, namely, visually observing and wearing the exoskeleton, elicits a brain response that can be classified. We develop a pipeline that can be applied to these two conditions and observe from our experiments that there is evidence for neural responses from electrodes near regions associated with ErrPs in an environment that resembles the real world. We found that these error-related responses can be classified as ErrPs with accuracies ranging from 60% to 71%, depending on the condition and the subject. Our pipeline could be further extended to detect and correct erroneous exoskeleton behavior in real-world settings.

Error-related potentials (ErrPs) provide an elegant method to improve human-machine interaction by detecting incorrect system behavior from the electroencephalogram of a human operator in real time. In this paper, we focus on adaptive subject-independent decoding models particularly suitable for ErrP classification. As individualized decoding models require a time-consuming calibration phase, such models provide a promising alternative. Based on an investigation of the characteristics of inter-subject variations in the signal and feature space, we evaluate the performance of a decoding model solely trained on prior data and the effectiveness of adapting this model to a new subject in a comparative study. Our results show that such a generalized model can decode ErrPs with an acceptable average accuracy of 72.7 ± 9.66% and that supervised adaptation can significantly improve the accuracy of the generalized model after adaptation with 85 trials by on average +3.8 ± 5.1%. We show that adaptation of subject-independent decoding models is superior to the traditional calibration procedure. Unsupervised adaptation only proved effective for some subjects and requires further attention to be practical for a broader range of subjects. Consequently, our work contributes to the development of calibration-free ErrP decoding in the broader scope of enhancing usability of ErrPs for human-machine interaction.

Advances in neuroscience are inspiring developments in robotics and vice versa.

In order to examine the formation of predictive motor memories, typical behavioural motor learning experiments perturb participants reaching movements using an external force field, to which they rapidly adapt, and exhibit after effects when the force field is removed. During the force field adaptation trials (Fig. 1), subjects move their hand from the start position (red circle) to the target position (black x) while a force field perturbs the movement (strength and direction indicated by yellow arrows) causing the hand to move to the final position (green circle). After the force field is removed, a washout effect is observed. While previous computational models can recreate the behavioral results, they do not account for the neural mechanisms involved. A computational model including a synaptic mechanism can help to explain the processes involved in motor learning. For this reason, we developed a bump-attractor, spiking neuron model of primary motor cortex (M1) proposing a synaptic mechanism using reward-based neurotransmitter release to explain motor adaptation and washout. The developed model consists of directionally-tuned neurons, shown to exist in M1 in biology, that encode the hand position through average neural firing. The force field is modeled through a simulated, external current perturbing the neural activity in the direction of the force field. In biology, Norepinephrine is released from locus coreuleus to M1 when errors are detected in the visual pathway. Norepinephrine affects M1 in a goal-directed manner, increasing the excitatory synaptic responses in the so-called hotspot, which is determined by arousal. For the model to remain close to biology, adaptation is modeled through an error-dependent increase in excitatory to excitatory conductance in the target position within the M1 model, leading to a decrease of the perturbation on the stable bump of neural activity across trials. Washout is implemented through a shift of the hotspot and the accumulated Norepinephrine through a motor-coordinate system shift during force field removal. After the initial washout trial, the wrongful coordinate system shift is detected and Norepinephrine in the shifted hotspot decays. The simulations from the proposed computational model qualitatively account for both: adaptation and washout as seen in comparison to the behavioural data from [1] (Fig. 1). Thus, the model suggests for the first time a biologically plausible synaptic mechanism in M1 that can explain the main features of motor learning of external dynamics.