EEG theta and Mu oscillations during perception of human and robot actions.

The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well

A large-scale evaluation framework for EEG deep learning architectures

EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many different architectures already published. Here we present a novel framework for the large-scale evaluation of different deep-learning architectures on different EEG datasets. This framework comprises (i) a collection of EEG datasets currently including 100 examples (recording sessions) from six different classification problems, (ii) a collection of different EEG decoding algorithms, and (iii) a wrapper linking the decoders to the data as well as handling structured documentation of all settings and (hyper-) parameters and statistics, designed to ensure transparency and reproducibility. As an applications example we used our framework by comparing three publicly available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow ConvNet, and two versions of EEGNet. We also show how our framework can be used to study similarities and differences in the performance of different decoding methods across tasks. We argue that the deep learning EEG framework as described here could help to tap the full potential of deep learning for BCI applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE SMC 2018 conferenc

Effects of Wrist Tendon Vibration on Targeted Upper-Arm Movements in Poststroke Hemiparesis

Background. Impaired motor control of the upper extremity after stroke may be related to lost sensory, motor, and integrative functions of the brain. Artificial activation of sensory afferents might improve control of movement by adding excitatory drive to sensorimotor control structures. The authors evaluated the effect of wrist tendon vibration (TV) on paretic upper-arm stability during point-to-point planar movements. Methods. TV (70 Hz) was applied to the forearm wrist musculature of 10 hemiparetic stroke patients as they made center-out planar arm movements. End-point stability, muscle activity, and grip pressure were compared as patients stabilized at the target position for trials completed before, during, and after the application of the vibratory stimulus. Results. Prior to vibration, hand position fluctuated as participants attempted to maintain the hand at the target after movement termination. TV improved arm stability, as evidenced by decreased magnitude of hand tangential velocity at the target. Improved stability was accompanied by a decrease in muscle activity throughout the arm as well as a mean decrease in grip pressure. Conclusions. These results suggest that vibratory stimulation of the distal wrist musculature enhances stability of the proximal arm and can be studied further as a mode for improving end-point stability during reaching in hemiparetic patients

How instructions modify perception: An fMRI study investigating brain areas involved in attributing human agency

Behavioural studies suggest that the processing of movement stimuli is influenced by beliefs about the agency behind these actions. The current study examined how activity in social and action related brain areas differs when participants were instructed that identicalmovement stimuli were either human or computer generated.Participants viewed a series of point-light animation figures derived frommotion-capture recordings of amoving actor, while functional magnetic resonance imaging (fMRI) was used to monitor patterns of neural activity. The stimuli were scrambled to produce a range of stimulus realism categories; furthermore, before each trial participants were told that they were about to view either a recording of human movement or a computersimulated pattern of movement. Behavioural results suggested that agency instructions influenced participants' perceptions of the stimuli. The fMRI analysis indicated different functions within the paracingulate cortex: ventral paracingulate cortex was more active for human compared to computer agency instructed trials across all stimulus types, whereas dorsal paracingulate cortex was activated more highly in conflicting conditions (human instruction, lowrealismor vice versa). These findings support the hypothesis that ventral paracingulate encodes stimuli deemed to be of human origin,whereas dorsal paracingulate cortex is involvedmore in the ascertainment of human or intentional agency during the observation of ambiguous stimuli. Our results highlight the importance of prior instructions or beliefs on movement processing and the role of the paracingulate cortex in integrating prior knowledge with bottom-up stimuli