Breathing is coupled with voluntary initiation of mental imagery

Attention to cardiac sensations enhances the heartbeat-evoked potential during exhalation

Respiration and cardiac activity intricately interact through complex physiological mechanisms. The heartbeat-evoked potential (HEP) is an EEG fluctuation reflecting the cortical processing of cardiac signals. We recently found higher HEP amplitude during exhalation than inhalation during a task involving attention to cardiac sensations. This may have been due to reduced cardiac perception during inhalation and heightened perception during exhalation through attentional mechanisms. To investigate relationships between HEP, attention, and respiration, we introduced an experimental setup that included tasks related to cardiac and respiratory interoceptive and exteroceptive attention. Results revealed HEP amplitude increases during the interoceptive tasks over fronto-central electrodes. When respiratory phases were taken into account, HEP increases were primarily driven by heartbeats recorded during exhalation, specifically during the cardiac interoceptive task, while inhalation had minimal impact. These findings emphasize the role of respiration in cardiac interoceptive attention and could have implications for respiratory interventions to fine-tune cardiac interoception.

Libet's legacy: A primer to the neuroscience of volition

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

Human motor augmentation with an extra robotic arm without functional interference

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality-based testing and training and the proposed pipeline.

Neurophysiological evidence of motor preparation in inner speech and the effect of content predictability

Self-generated overt actions are preceded by a slow negativity as measured by electroencephalogram, which has been associated with motor preparation. Recent studies have shown that this neural activity is modulated by the predictability of action outcomes. It is unclear whether inner speech is also preceded by a motor-related negativity and influenced by the same factor. In three experiments, we compared the contingent negative variation elicited in a cue paradigm in an active vs. passive condition. In Experiment 1, participants produced an inner phoneme, at which an audible phoneme whose identity was unpredictable was concurrently presented. We found that while passive listening elicited a late contingent negative variation, inner speech production generated a more negative late contingent negative variation. In Experiment 2, the same pattern of results was found when participants were instead asked to overtly vocalize the phoneme. In Experiment 3, the identity of the audible phoneme was made predictable by establishing probabilistic expectations. We observed a smaller late contingent negative variation in the inner speech condition when the identity of the audible phoneme was predictable, but not in the passive condition. These findings suggest that inner speech is associated with motor preparatory activity that may also represent the predicted action-effects of covert actions.

Low- and high-level coordination of orofacial motor actions

Orofacial motor actions are movements that, in rodents, involve whisking of the vibrissa, deflection of the nose, licking and lapping with the tongue, and consumption through chewing. These actions, along with bobbing and turning of the head, coordinate to subserve exploration while not conflicting with life-supporting actions such as breathing and swallowing. Orofacial and head movements are comprised of two additive components: a rhythm that can be entrained by the breathing oscillator and a broadband component that directs the actuator to the region of interest. We focus on coordinating the rhythmic component of actions into a behavior. We hypothesize that the precise timing of each constituent action is continually adjusted through the merging of low-level oscillator input with sensory-derived, high-level rhythmic feedback. Supporting evidence is discussed.