Elisabeth V. C. Friedrich

Near-infrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback

In the present study we implemented a real-time feedback system based on multichannel near-infrared spectroscopy (NIRS). Prior studies indicated that NIRS-based neurofeedback can enhance motor imagery related cortical activation. To specify these prior results and to confirm the efficacy of NIRS-based neurofeedback, we examined changes in blood oxygenation level collected in eight training sessions. One group got real feedback about their own brain activity (N=9) and one group saw a playback of another persons feedback recording (N=8). All participants performed motor imagery of a right hand movement. Real neurofeedback induced specific and focused brain activation over left motor areas. This focal brain activation became even more specific over the eight training sessions. In contrast, sham feedback led to diffuse brain activation patterns over the whole cortex. These findings can be useful when training patients with focal brain lesions to increase activity of specific brain areas for rehabilitation purpose.

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.

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.

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. Cronbachs 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. Cronbachs 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.

Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review

The use of serious games and virtual environments for learning is increasing worldwide. These technologies have the potential to collect live data from users through game play and can be combined with neuroscientific methods such as EEG, fNIRS and fMRI. The several learning processes triggered by serious games are associated with specific patterns of activation that distributed in time and space over different neural networks. This paper explores the opportunities offered and challenges posed by neuroscientific methods when capturing user feedback and using the data to create greater user adaptivity in game. Existing neuroscientific studies examining cortical correlates of game-based learning do not form a common or homogenous field. In contrast, they often have disparate research questions and are represented through a broad range of study designs and game genres. In this paper, the range of studies and applications of neuroscientific methods in game-based learning are reviewed.

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%).

Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity

Brain activity is considered the physical correlate of mental activity. Accordingly, a change of the state of mind implies a change in the state of the brain and vice versa. This homology between brain activity and cognitive tasks is very difficult to measure due to their very high complexity, temporal and spatial dynamics, as well as individual variability. However, the advancing of technology allows the detection of some states of brain activity with reasonable accuracy and their translation into control messages for devices external to the brain (i.e., brain-computer-interfaces, BCI). Moreover, studies on neurofeedback (NF) show that individuals can learn to modulate their own brain activity based on external feedback and thereby induce changes in cognition and behavior which can be used as therapy for various mental disorders. Interestingly, roughly about one third of people succeed in controlling computerized devices with brain signals right away; one third gains control after training and one third does not achieve useful control even by using state-of-the-art BCI or NF technology. Various factors have been related to the individual success but to date, no general theoretical framework is available. In this Research Topic, aspects of the training protocol such as instructions, task and feedback as well as psychological traits such as motivation, mood, locus of control, and empathy are investigated as determinants of BCI or NF performance. Moreover, the brain generates a large amount of coherent spontaneous activity independently of the BCI or NF task at hand which negatively impacts the reliable detection of brain activity patterns. Thus, the mechanisms and networks involved in gaining and maintaining control over brain activity as well as its prediction are addressed. Finally, as the ultimate goal of our research is to use BCI and NF for communication or control and therapy, respectively, novel applications for individuals with disabilities or disorders are discussed. The first part of the research topic deals with the role of the training protocol in BCI and NF. In the hypothesis and theory article of Lotte et al. (2013), problems in current BCI training protocols are identified according to instructional design principles and solutions for improved instruction, task, and feedback are proposed. Kober et al. (2013) report that the failure to describe a specific mental strategy when learning NF is indicative of better performance in an electroencephalographic (EEG)-based NF training paradigm controlled by modulation of the sensorimotor rhythm (SMR). Addressing the impact of the feedback in EEG-based BCIs, Koerner et al. (2014) demonstrate that the presentation of sham positive feedback resulted in different and better classifiable EEG patterns in comparison to sham negative feedback. Thus, the feedback success rate directly influences brain signals. The findings addressing the impact of psychological traits on performance are in line with the above presented results. Subramaniam and Vinogradov (2013) discuss in their review article how positive mood states change brain patterns and improve cognitive performance, which implies that mood states might also influence BCI and NF performance. Moreover, motivation and personality traits might also change brain patterns and performance. In contrast to earlier findings, Kleih and Kubler (2013) do not find a correlation between motivation and performance in an EEG-based P300-BCI. However, these authors report a negative correlation of empathic characteristics and P300 amplitudes. Also, Witte et al. (2013) demonstrate a negative correlation between the locus of control with regard to technology and SMR power in an EEG-based SMR-NF. The authors of both articles conclude that a high degree of empathy as well as high expectations regarding the locus of control might lead to emotional or cognitive overload, which, in turn, leads to lower performance in P300 or SMR-based BCIs. This is in line with the findings of Kober and colleagues addressing spontaneous mental tasks. Thus, a state of positive but not emotionally involved attentive and effortless relaxation might be the optimal state to control both NF and BCI. Besides external and internal factors influencing performance, neurophysiological as well as peripheral physiological correlates of gaining and maintaining control of brain activity are important to address. Ninaus et al. (2013) demonstrate in their study using functional magnetic resonance imaging (fMRI) that the fronto-parietal and cingulo-opercular network which are typically involved in cognitive control is active when participants believe to control a NF. Berman et al. (2013) examine the possibility to control the functionally localized anterior right insular cortex with fMRI-based NF. Also, peripheral physiological signals might have an impact on cognitive control. Pfurtscheller et al. (2013) investigate the interaction between brain and heart and suggest that the changes in heart rate in correlation with motor imagery can be used as indicator of mental effort to improve BCI control. To predict the ability to control a NF or BCI, Halder et al. (2013) performed MRI scans after one session of EEG-based SMR-BCI using motor imagery and report a positive correlation between individual BCI performance and the structural integrity and myelination quality of deep white matter structures. Also using MRI scans, Enriquez-Geppert et al. (2013) show that the volume of the midcingulate cortex as well as volume and concentration of the underlying white matter structures predicts EEG-based NF performance of frontal-midline theta performance. Both studies indicate that there is a neuroanatomical foundation for the aptitude to control a BCI or NF. In contrast to all mentioned research studies so far, Riccio et al. (2013) included individuals with amyotrophic lateral sclerosis to investigate predictor variables of performance in an EEG-based P300-BCI. They conclude that the temporal filtering capacity (i.e., ability to keep the attentional filter active during target selection) is crucial for BCI control. The last part covers potential applications for individuals with disabilities or mental disorders. Risetti et al. (2013) present findings of an EEG-based P300 auditory oddball paradigm to investigate residual unconscious and conscious cognitive function in individuals with a disorder of consciousness. Micoulaud-Franchi et al. (2013) propose in their perspective article to couple repetitive transcranial magnetic stimulation (rTMS) with NF and discuss therapeutic implications and ethical issues. In summary, this Research Topic illustrates how different factors impact BCI and NF performance and provides new perspectives that need addressing in the future.

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.

User-Appropriate and Robust Control Strategies to Enhance Brain−Computer Interface Performance and Usability

This project aimed to enhance performance and usability of mental imagery-based BCIs by evaluating (1) user-appropriate and robust control strategies, (2) whether mental imagery-based BCIs are robust and stable enough for real-world applications and (3) user-comfort in able-bodied and disabled individuals. Three studies were conducted to address these issues. The results showed that alternatives to motor imagery can provide a great benefit especially to severely motor impaired users. Individually chosen control strategies from a broad range of reliable and stable mental tasks can improve BCI usability and performance substantially. Furthermore, participants could operate the BCI while simultaneously perceiving or reacting to deviant auditory stimuli and could attain stable long-time BCI control despite longer breaks without any BCI use. This project paid special attention to practical issues and helped to pave the way out of the laboratory into real-world application for mental imagery-based BCIs.

The Potential Use of Neurophysiological Signals for Learning Analytics

Learning analytics is a very promising field for improving education, teaching and learning by collecting user data in serious games. However, the research on the use of physiological traces in learning analytics is sparse. Therefore, in this empirical explorative study we used functional near-infrared spectroscopy to examine if neurophysiological measurements can help to identify if a user is learning during playing a simple game. We identified increased brain activation in fronto-parietal areas while users actively learned rules or applied knowledge during gaming. In contrast, in a random condition, where users only reacted to the game without the possibility to learn or apply knowledge, increased brain activation was observed in central motor areas of the brain, reflecting the motoric interaction with the game per se. This preliminary neurophysiological empirical study showed that it is possible to use neurophysiological data to get insight on user learning during playing a very simple game. Future studies have to examine this effect in a more complex learning situation.