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Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities. [ABSTRACT FROM AUTHOR]

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time–frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation. [ABSTRACT FROM AUTHOR]

Motor awareness is a complex, multifaceted construct involving the awareness of both (i) one's motor state while executing a movement or remaining still and (ii) one's motor abilities. The analysis of neurological syndromes associated with motor disorders suggests the existence of various different components which are, however, integrated into a model of motor awareness. These components are: (i) motor intention, namely, a conscious desire to perform an action; (ii) motor monitoring and error recognition, that is, the capacity to check the execution of the action and identify motor errors; and (iii) a general awareness of one's own motor abilities and deficits, that is, the capacity to recognize the general state of one's motor abilities about the performance of specific actions and the potential consequences of motor impairment. Neuroanatomical correlates involving the parietal and insular cortices, the medial and lateral frontal regions, and subcortical structures (basal ganglia and limbic system) support this multi-component model. Specific damage (or disconnections) to these structures results in a number of different disorders in motor awareness, such as anosognosia for hemiplegia and apraxia, and a number of symptoms which are specific to motor intention disorders (e.g., the Anarchic Hand Syndrome and Tourette's Syndrome) or motor monitoring (e.g., Parkinson's and Huntington's diseases). All of these clinical conditions are discussed in the light of a motor awareness model. [ABSTRACT FROM AUTHOR]

The next generation of robotic agents, to employed both in industrial and service robotic applications, will be characterized by a high degree of Human–Robot Partnership that implies, for example, sharing common objectives, bidirectional flow of information, capability to learn from each other, and availability to mutual training. Moreover, there is a widespread feeling in the research community that probably Humans will not accept Robots as trustable Partners if they cannot ascribe some form of awareness and true understanding to them. This means that, in addition to the incremental improvements of Robotic-Bodyware, there will be the need for a substantial jump of the Robotic-Cogniware, namely a new class of Cognitive Architectures for Robots (CARs) that match the requirements and specific constraints of Human–Robot Partnership. The working hypothesis that underlies this paper is that such class of CARs must be bio-inspired, not in the sense of fine-grain imitation of neurobiology but the large framework of embodied cognition. In our opinion, trajectory/gesture formation should be one of the building blocks of bio-inspired CARs because biological motion is a fundamental channel of inter-human partnership, a true body language that allows mutual understanding of intentions. Moreover, one of the main concepts of embodied cognition, related to the importance of motor imagery, is that real (or overt) actions and mental (or covert) actions are generated by the same internal model and support the cognitive capabilities of human skilled subjects. The paper reviews the field of human trajectory formation, revealing in a novel manner the fil rouge that runs through motor neuroscience and proposes a computational framework for a robotic formulation that also addresses the Degrees of Freedom Problem and is formulated in terms of the force-field-based Passive Motion Paradigm.

Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients’ affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials.

Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients' affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials. [ABSTRACT FROM AUTHOR]

The next generation of robotic agents, to employed both in industrial and service robotic applications, will be characterized by a high degree of Human-Robot Partnership that implies, for example, sharing common objectives, bidirectional flow of information, capability to learn from each other, and availability to mutual training. Moreover, there is a widespread feeling in the research community that probably Humans will not accept Robots as trustable Partners if they cannot ascribe some form of awareness and true understanding to them. This means that, in addition to the incremental improvements of Robotic-Bodyware, there will be the need for a substantial jump of the Robotic-Cogniware, namely a new class of Cognitive Architectures for Robots (CARs) that match the requirements and specific constraints of Human-Robot Partnership. The working hypothesis that underlies this paper is that such class of CARs must be bio-inspired, not in the sense of fine-grain imitation of neurobiology but the large framework of embodied cognition. In our opinion, trajectory/gesture formation should be one of the building blocks of bio-inspired CARs because biological motion is a fundamental channel of inter-human partnership, a true body language that allows mutual understanding of intentions. Moreover, one of the main concepts of embodied cognition, related to the importance of motor imagery, is that real (or overt) actions and mental (or covert) actions are generated by the same internal model and support the cognitive capabilities of human skilled subjects. The paper reviews the field of human trajectory formation, revealing in a novel manner the fil rouge that runs through motor neuroscience and proposes a computational framework for a robotic formulation that also addresses the Degrees of Freedom Problem and is formulated in terms of the force-field-based Passive Motion Paradigm. [ABSTRACT FROM AUTHOR]

Decision-making is in the service of action regardless of whether the decision concerns perceptual information, goods or memories. Compared to recent advances in the neurobiology of perceptual or value-based decisions, however, the neural bases supporting the sampling of evidence in long-term memory, and the transformation of memory-based decisions into appropriate actions, are still poorly understood. In the present fMRI study, we used multivariate pattern analysis to investigate the temporal dynamics of choice- and action-predictive signals during an item recognition task that manipulated the association between memory choices (old/new) and motor responses (eye/hand) across subjects. Choice-predictive activity was mainly observed in striatal, lateral prefrontal and lateral parietal regions, was sensitive to the amount of decision evidence and showed a rapid increase after stimulus onset, followed by a fast decay. Action-predictive signals were found in primary sensory motor, premotor and occipito–parietal regions, were generally observed at the end of the decision phase and were not modulated by decision evidence. These findings suggest that a memory decision variable, potentially represented in a fronto–striato–parietal network, is not directly transformed into an action plan as often observed in perceptual decisions. Regions exhibiting choice predictive activity, and especially the striatum, however, also showed a second peak of decision-related activity that, unlike pure choice- or action-predictive signals, depended on the particular choice–response association. This second peak of activity in the striatum might represent the neural signature of the transformation of a memory decision into an appropriate motor response based on the specific choice–response association. [ABSTRACT FROM AUTHOR]

Voluntary actions can be fractionated in different phenomena: from the emergence of intentions and the ensuing motor plans and actions, to the anticipation and monitoring of their outcomes, to the appreciation of their congruency with intentions and to the eventual emergence of a sense of agency. It follows that motor intention and the sense of agency should occur at different stages in the normal generation of willed actions. Both these processes have been associated with a fronto-parietal motor network, but no study has investigated to what extent the two experiences can be dissociated for the brain regions involved. To this end, we assessed the PET/fMRI literature on agency and intentionality using a meta-analytic technique based on a hierarchical clustering algorithm. Beside a shared brain network involving the meso-frontal and prefrontal regions, the middle insula and subcortical structures, we found that motor intention and the sense of agency are functionally underpinned by separable sets of brain regions: an “intentionality network,” involving the rostral area of the mesial frontal cortex (middle cingulum and pre-supplementary motor area), the anterior insula and the parietal lobules, and a “self-agency network,” which involves the posterior areas of the mesial frontal cortex (the SMA proper), the posterior insula, the occipital lobe and the cerebellum. We were then able to confirm this functional organization by a subsequent seed-based fMRI resting-state functional connectivity analysis, with seeds derived from the intentionality/sense of agency specific clusters of the medial wall of the frontal lobe. Our results suggest the existence of a rostro-caudal gradient within the mesial frontal cortex, with the more anterior regions linked to the concept of motor intentionality and the brain areas located more posteriorly associated with the direct monitoring between the action and its outcome. This suggestion is reinforced by the association between the sense of agency and the activation of the occipital lobes, to suggest a direct comparison between the movement and its external (e.g., visual) consequences. The shared network may be important for the integration of intentionality and agency in a coherent appreciation of self-generated actions.

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

When we reach to grasp something, we need to take into account both the properties of the object we are grasping and the intention we have in mind. Previous research has found these constraints to be visible in the reach-to-grasp kinematics, but there is no consensus on which kinematic parameters are the most sensitive. To examine this, a systematic literature search and meta-analyses were performed. The search identified studies assessing how changes in either an object property or a prior intention affect reach-to-grasp kinematics in healthy participants. Hereafter, meta-analyses were conducted using a restricted maximum likelihood random effect model. The meta-analyses showed that changes in both object properties and prior intentions affected reach-to-grasp kinematics. Based on these results, the authors argue for a tripartition of the reach-to-grasp movement in which the accelerating part of the reach is primarily associated with transporting the hand to the object (i.e., extrinsic object properties), the decelerating part of the reach is used as a preparation for object manipulation (i.e., prepare the grasp or the subsequent action), and the grasp is associated with manipulating the object's intrinsic properties, especially object size. [ABSTRACT FROM PUBLISHER]

Electrophysiological and neuroimaging studies suggest that our actions are initiated by unconscious mental processes long before awareness of intention to act. The time window between the awareness of the intention to move and the movement onset, which normally permits to exert a conscious “veto” on the impending action, is modulated by individual differences in trait impulsivity. In particular, trait impulsive people show a delayed awareness of the intention to act, probably exceeding the “point of no return”, after which the action can no longer be inhibited. In order to investigate if individual differences in the “veto” interval might be explained by differences in the readiness potential (RP) dynamics, nineteen healthy participants underwent an impulsivity trait assessment using the Barratt Impulsiveness Scale (BIS-11) and performed a task based on Libet’s clock paradigm, during EEG recordings of pre-movement neural activity. We observed a positive relationship between impulsive personality trait and motor system excitability during the preparation of self-initiated movements. In particular, the RP showed an earlier negative rising phase and a greater amplitude, with the increasing of BIS-11 scores. Based on present results, we conclude hypothesizing that trait impulsivity might be characterized by less effective preparatory inhibition mechanisms, which have a fundamental role in the control of behavior. [ABSTRACT FROM AUTHOR]

Comparison between the intended and performed motor action can be expected to occur in the final epoch of a voluntary movement. In search for electrophysiological correlates of this mental process the purpose of the current study was to identify intracerebral sites activated in final epoch of self-paced voluntary movement. Intracerebral EEG was recorded from 235 brain regions of 42 epileptic patients who performed self-paced voluntary movement task. Evoked potentials starting at 0 to 243 ms after the peak of averaged, rectified electromyogram were identified in 21 regions of 13 subjects. The mean amplitude value of these late movement potentials (LMP) was 56.4 ± 27.5 μV. LMPs were observed in remote regions of mesiotemporal structures, cingulate, frontal, temporal, parietal, and occipital cortices. Closely before the LMP onset, a significant increase of phase synchronization was observed in all EEG record pairs in 9 of 10 examined subjects; p < 0.001, Mann-Whitney U test. In conclusion, mesiotemporal structures, cingulate, frontal, temporal, parietal, and occipital cortices seem to represent integral functionally linked parts of network activated in final epoch of self-paced voluntary movement. Activation of this large-scale neuronal network was suggested to reflect a comparison process between the intended and actually performed motor action. Our results contribute to better understanding of neural mechanisms underlying goal-directed behavior crucial for creation of agentive experience. [ABSTRACT FROM AUTHOR]

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes. Yet, the distinct functional roles of phase, amplitude and PAC across the planning and execution of goal-directed motor behavior remain largely elusive. Here, we address this question with unprecedented resolution thanks to multi-site intracerebral EEG recordings in human subjects while they performed a delayed motor task. To compare the roles of phase, amplitude and PAC, we monitored intracranial brain signals from 748 sites across six medically intractable epilepsy patients at movement execution, and during the delay period where motor intention is present but execution is withheld. In particular, we used a machine-learning framework to identify the key contributions of various neuronal responses. We found a high degree of overlap between brain network patterns observed during planning and those present during execution. Prominent amplitude increases in the delta (2–4 Hz) and high gamma (60–200 Hz) bands were observed during both planning and execution. In contrast, motor alpha (8–13 Hz) and beta (13–30 Hz) power were suppressed during execution, but enhanced during the delay period. Interestingly, single-trial classification revealed that low-frequency phase information, rather than spectral power change, was the most discriminant feature in dissociating action from intention. Additionally, despite providing weaker decoding, PAC features led to statistically significant classification of motor states, particularly in anterior cingulate cortex and premotor brain areas. These results advance our understanding of the distinct and partly overlapping involvement of phase, amplitude and the coupling between them, in the neuronal mechanisms underlying motor intentions and executions. [ABSTRACT FROM AUTHOR]

Motion perception is a fundamental feature of the human visual system. As part of our daily life we often have to determine the direction of motion, even in ambiguous situations. These situations force us to rely on exogenous cues, such as other environmental motion, endogenous cues, such as our own actions, or previously learned experiences. In three experiments, we asked participants to report the direction of an ambiguous motion display, manipulating exogenous and endogenous sources of information. Specifically, in all three experments the exogenous information was represented by another motion cue while the endogenous cue was represented respectively by movement execution, movement planning, or a learned association about the motion display. Participants were consistently biased by stable motion cues in the environment when reporting the ambiguous target direction. In the absence of stable exogenous motion information, participants were biased by their motor movements and even the planning of such movements. However, when participants learned a specific association about the target motion, this acquired endogenous knowledge countered exogenous motion cues in biasing participants’ perception. Taken together, our findings demonstrate that we disambiguate ambiguous motion using different sources of exogenous and endogenous cues, and that learned associations may be particularly salient in countering the biasing effects of environmental cues.

Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35-\mum CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by \approx 2X. [ABSTRACT FROM PUBLISHER]

Although dopamine plays an important role for motor control and modulates the frontal function via basal ganglia-thalamo-cortical loop, it is not known whether dopamine can affect the awareness of motor intention or not. To test this hypothesis, we applied Libet's clock paradigm to Parkinson's disease (PD) patients. Thirteen PD patients and 13 age-matched, healthy controls took part in the experiment which consisted of three judgment paradigms: W, M and S judgment. In W and M judgments, subjects were asked to press the key at self-willed timing. In W judgment, subjects reported the location of the clock's hand when they became aware of the intention (W-time). In M judgment, subjects reported the time when they became aware of the actual movement (M-time). In S judgment, subjects reported the time of the electrical stimulation given to their hand (S-time). W-time was significantly shorter in PD patients than in healthy control subjects while M-time and S-time were not different between the two groups. Delayed awareness of motor intention but not of action in PD patients might be related to dopamine depletion in those patients. [ABSTRACT FROM AUTHOR]

When humans simultaneously perform different movements with both hands, each limb movement interferes with the contralateral limb movement (bimanual coupling). Previous studies on both healthy volunteers and patients with central or peripheral nervous lesions suggested that such motor constraints are tightly linked to intentional motor programs, rather than to movement execution. Here, we aim to investigate this phenomenon, by using a circles-lines task in which, when subjects simultaneously draw lines with the right hand and circles with the left hand, both the trajectories tend to become ovals (bimanual coupling effect). In a first group, we immobilized the subjects’ left arm with a cast and asked them to try to perform the bimanual task. In a second group, we passively moved the subjects’ left arm and asked them to perform voluntary movements with their right arm only. If the bimanual coupling arises from motor intention and planning rather than spatial movements, we would expect different results in the two groups. In the Blocked group, where motor intentionality was required but movements in space were prevented by immobilization of the arm, a significant coupling effect (i.e., a significant increase of the ovalization index for the right hand lines) was found. On the contrary, in the Passive group, where movements in space were present but motor intentionality was not required, no significant coupling effect was observed. Our results confirmed, in healthy subjects, the central role of the intentional and predictive operations, already evidenced in pathological conditions, for the occurrence of bimanual coupling. [ABSTRACT FROM AUTHOR]

When given a choice between actions that yield the same reward, we tend to prefer the one that requires the least effort. Recent studies have shown that humans are remarkably accurate at evaluating the effort of potential reaching actions and can predict the subtle energetic demand caused by the nonisotropic biomechanical properties of the arm. In the present study, we investigated the time course over which such information is computed and comes to influence decisions. Two independent approaches were used. First, subjects performed a reach decision task in which the time interval for deciding between two candidate reaching actions was varied from 200 to 800 ms. Second, we measured motor-evoked potential (MEPs) to single-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) to probe the evolving decision at different times after stimulus presentation. Both studies yielded a consistent conclusion: that a prediction of the effort associated with candidate movements is computed very quickly and influences decisions within 200 ms after presentation of the candidate actions. Furthermore, whereas the MEPs measured 150 ms after stimulus presentation were well correlated with the choices that subjects ultimately made, later in the trial the MEP amplitudes were primarily related to the muscular requirements of the chosen movement. This suggests that corticospinal excitability (CSE) initially reflects a competition between candidate actions and later changes to reflect the processes of preparing to implement the winning action choice [ABSTRACT FROM AUTHOR]

Highlights: [•] We describe the fMRI patterns of anosognosia in right brain stroke patients. [•] The patients were studied while claiming to have just moved the paralysed hand. [•] The false belief of having moved was preceded by activity of specific motor cortices. [•] Motor cortex activity contributes to our beliefs about the state of the motor system. [•] We discuss the generalisation that specific systems contribute to specific beliefs. [ABSTRACT FROM AUTHOR]

In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain–computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation. [ABSTRACT FROM PUBLISHER]

The capacity to ascribe goals and intentions to others is a fundamental step in child cognitive development. The aim of the present study was to assess the age at which these capabilities are acquired in typically developing children. Two experiments were carried out. In the first experiment, 4 groups of children (age range = 3 years 2 months-7 years 11 months) were shown pictures representing hand-object interactions and asked what the individual was doing (what task) and why {why task). In the why task, observed handgrip could be either congruent with the most typical action performed with that object (e.g., to drink in the case of a mug) or corresponding to the act of putting away the object. In the second experiment, children saw pictures showing a handgrip either within a context suggesting the most typical use of the object or its being put away. Results showed that by 3-4 years, children are able to state the goal relatedness of an observed motor act (what understanding), whereas the ability to report the intention underlying it (why understanding) is a later and gradual acquisition, reaching a high performance by 6-7 years. These results, besides their intrinsic value, provide an important baseline for comparisons with studies on developmental disorders, also highlighting the relevance of distinguishing what and why understanding. [ABSTRACT FROM AUTHOR]

Highlights: [•] We examine whether the intention of use is centred on hand- or tool-action effect representations. [•] Participants were asked to perform a motor intention paradigm. [•] They had to use two types of pliers (normal vs. inverse) to move an object. [•] Results suggest that their intention to use the pliers activate tool-action effect representations. [Copyright &y& Elsevier]

In learning models of strategic game play, an agent constructs a valuation (action value) over possible future choices as a function of past actions and rewards. Choices are then stochastic functions of these action values. Our goal is to uncover a neural signal that correlates with the action value posited by behavioral learning models. We measured activity from neurons in the superior colliculus (SC), a midbrain region involved in planning saccadic eye movements, in monkeys while they performed two saccade tasks. In the strategic task, monkeys competed against a computer in a saccade version of the mixed-strategy game “matching-pennies”. In the instructed task, stochastic saccades were elicited through explicit instruction rather than free choices. In both tasks, neuronal activity and behavior were shaped by past actions and rewards with more recent events exerting a larger influence. Further, SC activity predicted upcoming choices during the strategic task and upcoming reaction times during the instructed task. Finally, we found that neuronal activity in both tasks correlated with an established learning model, the Experience Weighted Attraction model of action valuation (Ho, Camerer, and Chong, 2007). Collectively, our results provide evidence that action values hypothesized by learning models are represented in the motor planning regions of the brain in a manner that could be used to select strategic actions.

Abstract: This paper is devoted to an assessment of the three-step model offered by Gallese and colleagues in support of the thesis that the function of the mirror mechanism is to mindread an agent’s intention. The first step of the model is the acceptance of the direct-matching model of action understanding. The second step is the endorsement of a different model of mirror neuron activity, i.e. the model of chains of logically related mirror neurons (or motor chains) whose application to action-mirroring is supposed to show that the mirror mechanism enables an observer to predict the goal of the agent’s forthcoming action. The third step is the endorsement of the ‘deflationary’ account of intention-ascription according to which to ascribe an intention to an agent is to predict the goal of the agent’s forthcoming action. I argue that each step of the model faces insuperable objections. [Copyright &y& Elsevier]

Highlights: [•] Direct electrical stimulation (DES) is an invaluable method for neuroscience. [•] Claims that DES is a poorly understood, potentially misleading method are unfounded. [•] DES has specific, causally interpretable effects at well-defined anatomical sites. [•] DES also reduces dramatically postoperative deficits in brain-damaged patients. [Copyright &y& Elsevier]

Abstract: Fresh empirical evidence supports the notion that fear of movement-related pain can be acquired through associative learning. In the context of these findings, 2 ideas are appealing, yet uninvestigated. The first is that merely the intention to perform a painful movement acts as a covert conditioned stimulus (CS) inducing defensive fear responses (ie, gaining excitatory properties following Pavlovian acquisition). The second idea is that after extinction, fear of movement-related pain can easily be reinstated after unexpected painful stimuli (ie, reinstatement). In a voluntary differential conditioning movement paradigm with movements as CSs and a painful electrocutaneous stimulus as the unconditioned stimulus (pain-US), 2 groups were included (Experimental/Control). One movement (CS+) was followed by the pain-US and another movement (CS−) was not during acquisition, while the CS+ was no longer reinforced during extinction. Next, the Experimental group received 2 reinstating pain-USs, whereas the Control group did not. The CS+ but not the CS− evoked fear of movement-related pain in self-reports and eye-blink startles. Intriguingly, the mere intention to perform the painful movement produced higher eye-blink startle responses than the intention to perform the nonpainful movement. We also demonstrated nondifferential reinstatement in the verbal fear ratings in the Experimental group only. Perspective: This study demonstrates that the mere intention to perform a painful movement prior to the actual painful movement itself can come to elicit conditioned fear responses. These results suggest that actual movement may not be necessary to elicit pain-related fear responses, maintaining chronic pain-related fear, avoidance, and disability. [Copyright &y& Elsevier]

Objective . This study analyzed detection (movement vs. non-movement) and classification (different types of movements) to decode upper-limb movement volitions in a pseudo-online fashion. Approach . Nine healthy subjects executed four self-initiated movements: left wrist extension, right wrist extension, left index finger extension, and right index finger extension. For detection, we investigated the performance of three individual classifiers (support vector machine (SVM), EEGNET, and Riemannian geometry featured SVM) on three frequency bands (0.05-5 Hz, 5-40 Hz, 0.05-40 Hz). The best frequency band and the best classifier combinations were constructed to realize an ensemble processing pipeline using majority voting. For classification, we used adaptive boosted Riemannian geometry model to differentiate contra-lateral and ipsilateral movements. Main results . The ensemble model achieved 79.6 ± 8.8% true positive rate and 3.1 ± 1.2 false positives per minute with 75.3 ± 112.6 ms latency on a pseudo-online detection task. The following classification gave around 67% accuracy to differentiate contralateral movements. Significance . The newly proposed ensemble method and pseudo-online testing procedure could provide a robust brain-computer interface design for movement decoding., (Creative Commons Attribution license.)

Since the birth of Experimental Psychology at the end of the 19th century, illusions entered the paradigms and methods of the discipline, and the new trends in cognitive sciences reinforced the status of a true science of the illusions (Ninio, 1998). This contribution will dwell first on classical bistable illusions (Rubin's vase and Necker's cube), for they feed the basic conception of our perceptual senses as a closed loop system (à la Rodolfo Llinás, 2003). The weird sensation of phantom limbs in amputees, phantom bodies in different brain states, as well as the production of the rubberhand illusion in intact people, and also self/ alien body presence created by virtual reality designs, are all phenomena which can assist with testing data-driven models. It would thus seem possible to distinguish between cases when cognitive sciences tells us to believe the subject report (e.g. phantom limb pain), and when they tell us not to believe her/ him anymore (e.g. free will in the motor intention). [ABSTRACT FROM AUTHOR]

We investigated whether individuals with a mild form of autism spectrum disorder (ASD) are influenced by an actor's gaze direction when anticipating how an observed action will continue in the immediate future. Participants observed a head rotate towards them, while the gaze direction was either leading, or lagging behind, rotation. They also observed identical rotations of a cylinder containing the geometrical equivalent of the gaze manipulation. The control group was influenced by the gaze manipulations for the animate but not the inanimate stimulus. The ASD group did not discriminate between the stimuli, showing a similar influence for both. This suggests that the ASD responses in the animate condition were biased by the low-level directional features of the eyes rather than by the conveyed intentions. [ABSTRACT FROM AUTHOR]