TMS-induced inhibition of the left premotor cortex modulates illusory social perception.

Communicative actions from one person are used to predict another person's response. However, in some cases, these predictions can outweigh the processing of sensory information and lead to illusory social perception such as seeing two people interact, although only one is present (i.e., seeing a Bayesian ghost). We applied either inhibitory brain stimulation over the left premotor cortex (i.e., real TMS) or sham TMS. Then, participants indicated the presence or absence of a masked agent that followed a communicative or individual gesture of another agent. As expected, participants had more false alarms in the communicative (i.e., Bayesian ghosts) than individual condition in the sham TMS session and this difference between conditions vanished after real TMS. In contrast to our hypothesis, the number of false alarms increased (rather than decreased) after real TMS. These pre-registered findings confirm the significance of the premotor cortex for social action predictions and illusory social perception.

Spatio-temporal dynamics of oscillatory brain activity during the observation of actions and interactions between point-light agents.

Predicting actions from non-verbal cues and using them to optimise one's response behaviour (i.e. interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a 'communicative' or an 'individual' action. The second agent either was blended into a cluster of noise dots (i.e. present) or was entirely replaced by noise dots (i.e. absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalising processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimised treatment approaches for individuals with problems in social interactions.

Seeing a Bayesian ghost: Sensorimotor activation leads to an illusory social perception.

Based on our prior experiences we form social expectations and anticipate another person's response. Under certain conditions, these expectations can be so strong that they lead to illusory perception of another person who is actually not there (i.e., seeing a Bayesian ghost). We used EEG to investigate the neural correlates of such illusory social perception. Our results showed that activation of the premotor cortex predicted the occurrence of the Bayesian ghost, whereas its actual appearance was later accompanied by activation in sensorimotor and adjacent parietal regions. These findings confirm that our perception of others is so strongly affected by prior expectations, in such a way they can prompt illusory social perceptions associated with activity change in brain regions relevant for action perception. They also contribute to a better understanding of social interaction in healthy individuals as well as persons with mental illnesses, which can be characterized by illusory perception and social interaction difficulties.

Theta:gamma phase coupling and evoked gamma activity reflect the fidelity of mental templates during memory matching in visual perception.

Top-down predictions of future events shaped by prior experience are an important control mechanism to allocate limited attentional resources more efficiently and are thought to be implemented as mental templates stored in memory. Increased evoked gamma activity and theta:gamma phase-phase coupling over parieto-occipital areas have previously been observed when mental templates meet matching visual stimuli. Here, we investigated how these signatures evolve during the formation of new mental templates and how they relate to the fidelity of such. Based on single-trial feedback, participants learned to classify target shapes as matching or mismatching with preceding cue sequences. In the end of the experiment, they were asked to freely reproduce targets as means of template fidelity. We observed fidelity-dependent increments of matching-related gamma phase locking and theta:gamma phase coupling in early visual areas around 100-200-ms poststimulus over time. Theta:gamma phase synchronization and evoked gamma activity might serve as complementary signatures of memory matching in visual perception; theta:gamma phase synchronization seemingly most important in early phases of learning and evoked gamma activity being essential for transition of mental templates into long-term memory.

Does cross-frequency phase coupling of oscillatory brain activity contribute to a better understanding of visual working memory?

Nesting of fast rhythmical brain activity (gamma) into slower brain waves (theta) has frequently been suggested as a core mechanism of multi-item working memory (WM) retention. It provides a better understanding of WM capacity limitations, and, as we discuss in this review article, it can lead to applications for modulating memory capacity. However, could cross-frequency coupling of brain oscillations also constructively contribute to a better understanding of the neuronal signatures of working memory compatible with theoretical approaches that assume flexible capacity limits? Could a theta-gamma code also be considered as a neural mechanism of flexible sharing of cognitive resources between memory representations in multi-item WM? Here, we propose potential variants of theta-gamma coupling that could explain WM retention beyond a fixed memory capacity limit of a few visual items. Moreover, we suggest how to empirically test these predictions in the future.

An Effective Neurofeedback Intervention to Improve Social Interactions in Children with Autism Spectrum Disorder.

Neurofeedback training (NFT) approaches were investigated to improve behavior, cognition and emotion regulation in children with autism spectrum disorder (ASD). Thirteen children with ASD completed pre-/post-assessments and 16 NFT-sessions. The NFT was based on a game that encouraged social interactions and provided feedback based on imitation and emotional responsiveness. Bidirectional training of EEG mu suppression and enhancement (8-12 Hz over somatosensory cortex) was compared to the standard method of enhancing mu. Children learned to control mu rhythm with both methods and showed improvements in (1) electrophysiology: increased mu suppression, (2) emotional responsiveness: improved emotion recognition and spontaneous imitation, and (3) behavior: significantly better behavior in every-day life. Thus, these NFT paradigms improve aspects of behavior necessary for successful social interactions.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment.

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

Neurorehabilitation of social dysfunctions: a model-based neurofeedback approach for low and high-functioning autism.

Autism Spectrum Disorder (ASD) is an increasingly prevalent condition with core deficits in the social domain. Understanding its neuroetiology is critical to providing insights into the relationship between neuroanatomy, physiology and social behaviors, including imitation learning, language, empathy, theory of mind, and even self-awareness. Equally important is the need to find ways to arrest its increasing prevalence and to ameliorate its symptoms. In this review, we highlight neurofeedback studies as viable treatment options for high-functioning as well as low-functioning children with ASD. Lower-functioning groups have the greatest need for diagnosis and treatment, the greatest barrier to communication, and may experience the greatest benefit if a treatment can improve function or prevent progression of the disorder at an early stage. Therefore, we focus on neurofeedback interventions combined with other kinds of behavioral conditioning to induce neuroplastic changes that can address the full spectrum of the autism phenotype.

Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum.

Individuals with autism spectrum disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate and its variability, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body, and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced.

Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually.

This study implemented a systematic user-centered training protocol for a 4-class brain-computer interface (BCI). The goal was to optimize the BCI individually in order to achieve high performance within few sessions for all users. Eight able-bodied volunteers, who were initially naïve to the use of a BCI, participated in 10 sessions over a period of about 5 weeks. In an initial screening session, users were asked to perform the following seven mental tasks while multi-channel EEG was recorded: mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, motor imagery of the left hand and motor imagery of both feet. Out of these seven mental tasks, the best 4-class combination as well as most reactive frequency band (between 8-30 Hz) was selected individually for online control. Classification was based on common spatial patterns and Fisher's linear discriminant analysis. The number and time of classifier updates varied individually. Selection speed was increased by reducing trial length. To minimize differences in brain activity between sessions with and without feedback, sham feedback was provided in the screening and calibration runs in which usually no real-time feedback is shown. Selected task combinations and frequency ranges differed between users. The tasks that were included in the 4-class combination most often were (1) motor imagery of the left hand (2), one brain-teaser task (word association or mental subtraction) (3), mental rotation task and (4) one more dynamic imagery task (auditory imagery, spatial navigation, imagery of the feet). Participants achieved mean performances over sessions of 44-84% and peak performances in single-sessions of 58-93% in this user-centered 4-class BCI protocol. This protocol is highly adjustable to individual users and thus could increase the percentage of users who can gain and maintain BCI control. A high priority for future work is to examine this protocol with severely disabled users.

Long-term evaluation of a 4-class imagery-based brain-computer interface.

OBJECTIVE: The study aimed to improve brain-computer interface (BCI)-usability by using distinct control strategies and evaluating performance, brain activity and psychological variables on a long-term basis over several months. METHODS: Fourteen able-bodied users participated in 10 sessions, plus a follow-up session. Users were trained to control an EEG-based 4-class BCI with the mental tasks, word association, mental subtraction, spatial navigation, and motor imagery. RESULTS: Eight users reached mean accuracies of 61-72% and managed to control all 4 classes above chance in single-sessions. Performance and brain patterns stayed stable over 10 weeks without training. Motor imagery showed the best performance and most distinct brain patterns. Participants' fear of incompetence decreased while the quality of their imagery and task ease increased over sessions. The evaluation of feedback differed between tasks and correlated with performance. CONCLUSION: Users can control a real-time 4-class BCI, driven by distinct mental tasks, with stable performance over months. However, general performance was rather low for effective BCI control in daily life. Possibilities for future optimizations to increase performance are discussed. SIGNIFICANCE: The evaluation of alternatives to motor imagery, long-term BCI use, and psychological variables is important to improve usability for mental imagery-based BCIs.

Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks.

OBJECTIVE: The aim of this study was to examine the temporal stability of event-related desynchronization/synchronization (ERD/S) patterns over several sessions as a function of mental task, frequency band, brain region and time interval during the imagery period. METHODS: Nine volunteers participated in four sessions within 2 weeks of multi-channel EEG recordings. They performed seven mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces, motor imagery) during 7-s imagery periods. Cronbach's alpha coefficients were calculated over sessions to evaluate the stability of ERD/S values. RESULTS: The word association, mental subtraction and spatial navigation task showed highest stability. Cronbach's alpha coefficients were highest in the alpha bands (7-10, 10-13 Hz), poorer in the beta bands (13-20, 20-30 Hz) and poorest in the theta band (4-7 Hz). In the majority of tasks, the first time interval and posterior left regions showed highest stability and strongest ERD in the alpha and beta bands. CONCLUSION: Stability of ERD/S is strongly dependent on the specific task and differs between time intervals of the imagery period. Furthermore, stability was related to ERD in the alpha and beta bands. SIGNIFICANCE: The reliability of brain activation patterns is highly relevant for brain-computer interface developments.

The effect of distinct mental strategies on classification performance for brain-computer interfaces.

Motor imagery is the task most commonly used to induce changes in electroencephalographic (EEG) signals for mental imagery-based brain computer interfacing (BCI). In this study, we investigated EEG patterns that were induced by seven different mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces and motor imagery) and evaluated the binary classification performance. The aim was to provide a broad range of reliable and user-appropriate tasks to make individual optimization of BCI control strategies possible. Nine users participated in four sessions of multi-channel EEG recordings. Mental tasks resulting most frequently in good binary classification performance include mental subtraction, word association, motor imagery and mental rotation. Our results indicate that a combination of 'brain-teasers' - tasks that require problem specific mental work (e.g. mental subtraction, word association) - and dynamic imagery tasks (e.g. motor imagery) result in highly distinguishable brain patterns that lead to an increased performance.

Impact of auditory distraction on user performance in a brain-computer interface driven by different mental tasks.

OBJECTIVE: Mental imagery-based brain-computer interface (BCI)-protocols mostly allow users to focus on the task without external interferences. Environmental stimuli, however, may hamper users' ability to generate proper brain activity patterns. The aim of this study was to investigate whether users are able to retain satisfactory BCI control during auditory distraction, and whether distinct mental tasks are affected differently from auditory distraction. METHODS: Twelve participants controlled a 4-class BCI with the mental tasks word association, mental subtraction, spatial navigation and motor imagery by modulation of EEG frequency bands in 10 sessions. Simultaneously to the imagery task, users had to either ignore all tones (passive distraction) that were presented according to an oddball paradigm or react upon the target tone (active distraction). RESULTS: Passive distraction led to an increased user performance compared to active distraction and no distraction condition. Differences between motor imagery and the other three mental tasks in performance were reflected in the P300 amplitude, latency and reaction time and thus might indicate differences in workload. CONCLUSION: Auditory distraction had no adverse effect on the BCI performance of the examined mental task. SIGNIFICANCE: Our results are encouraging for real-world application as participants succeeded in operating the 4-class BCI during auditory distraction.

A scanning protocol for a sensorimotor rhythm-based brain-computer interface.

The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.