147 research outputs found

    Improving cognitive control: Is theta neurofeedback training associated with proactive rather than reactive control enhancement?

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    Frontal-midline (FM) theta activity (4–8 Hz) is proposed to reflect a mechanism for cognitive control that is needed for working memory retention, manipulation, and interference resolution. Modulation of FM theta activity via neurofeedback training (NFT) demonstrated transfer to some but not all types of cognitive control. Therefore, the present study investigated whether FM theta NFT enhances performance and modulates underlying EEG characteristics in a delayed match to sample (DMTS) task requiring mainly proactive control and a color Stroop task requiring mainly reactive control. Moreover, temporal characteristics of transfer were explored over two posttests. Across seven 30-min NFT sessions, an FM theta training group exhibited a larger FM theta increase compared to an active control group who upregulated randomly chosen frequency bands. In a posttest performed 13 days after the last training session, the training group showed better retention performance in the DMTS task. Furthermore, manipulation performance was associated with NFT theta increase for the training but not the control group. Contrarily, behavioral group differences and their relation to FM theta change were not significant in the Stroop task, suggesting that NFT is associated with proactive but not reactive control enhancement. Transfer to both tasks at a posttest one day after training was not significant. Behavioral improvements were not accompanied by changes in FM theta activity, indicating no training-induced modulation of EEG characteristics. Together, these findings suggest that NFT supports transfer to cognitive control that manifests late after training but that other training-unspecific factors may also contribute to performance enhancement

    Higher-order brain areas associated with real-time functional MRI neurofeedback training of the somato-motor cortex

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    AbstractNeurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes.Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning.The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC.Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings

    Making sense of real-time functional magnetic resonance imaging (rtfMRI) and rtfMRI neurofeedback

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    This review explains the mechanism of functional magnetic resonance imaging in general and specifically introduces real-time functional magnetic resonance imaging as a method for training self-regulation of brain activity. Using real-time functional magnetic resonance imaging neurofeedback, participants can acquire control over their own brain activity. In patients with neuropsychiatric disorders, this control can potentially have therapeutic implications. In this review, the technical requirements are presented and potential applications and limitations are discussed

    Operant EEG-based BMI: Learning and consolidating device control with brain activity

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    "Whether you are reading this thesis on paper or screen, it is easy to take for granted all the highly specialized movements you are doing at this very moment just to go through each page. Just to turn a page, you have to reach for and grasp it, turn it and let go at the precise moment not to rip it.(...)

    Improving cognitive control: Is theta neurofeedback training associated with proactive rather than reactive control enhancement?

    Get PDF
    Frontal-midline (FM) theta activity (4–8 Hz) is proposed to reflect a mechanism for cognitive control that is needed for working memory retention, manipulation, and interference resolution. Modulation of FM theta activity via neurofeedback training (NFT) demonstrated transfer to some but not all types of cognitive control. Therefore, the present study investigated whether FM theta NFT enhances performance and modulates underlying EEG characteristics in a delayed match to sample (DMTS) task requiring mainly proactive control and a color Stroop task requiring mainly reactive control. Moreover, temporal characteristics of transfer were explored over two posttests. Across seven 30-min NFT sessions, an FM theta training group exhibited a larger FM theta increase compared to an active control group who upregulated randomly chosen frequency bands. In a posttest performed 13 days after the last training session, the training group showed better retention performance in the DMTS task. Furthermore, manipulation performance was associated with NFT theta increase for the training but not the control group. Contrarily, behavioral group differences and their relation to FM theta change were not significant in the Stroop task, suggesting that NFT is associated with proactive but not reactive control enhancement. Transfer to both tasks at a posttest one day after training was not significant. Behavioral improvements were not accompanied by changes in FM theta activity, indicating no training-induced modulation of EEG characteristics. Together, these findings suggest that NFT supports transfer to cognitive control that manifests late after training but that other training-unspecific factors may also contribute to performance enhancement

    Serious Games and Mixed Reality Applications for Healthcare

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    Virtual reality (VR) and augmented reality (AR) have long histories in the healthcare sector, offering the opportunity to develop a wide range of tools and applications aimed at improving the quality of care and efficiency of services for professionals and patients alike. The best-known examples of VR–AR applications in the healthcare domain include surgical planning and medical training by means of simulation technologies. Techniques used in surgical simulation have also been applied to cognitive and motor rehabilitation, pain management, and patient and professional education. Serious games are ones in which the main goal is not entertainment, but a crucial purpose, ranging from the acquisition of knowledge to interactive training.These games are attracting growing attention in healthcare because of their several benefits: motivation, interactivity, adaptation to user competence level, flexibility in time, repeatability, and continuous feedback. Recently, healthcare has also become one of the biggest adopters of mixed reality (MR), which merges real and virtual content to generate novel environments, where physical and digital objects not only coexist, but are also capable of interacting with each other in real time, encompassing both VR and AR applications.This Special Issue aims to gather and publish original scientific contributions exploring opportunities and addressing challenges in both the theoretical and applied aspects of VR–AR and MR applications in healthcare

    Brain-Computer Interface

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    Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

    Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Cortex

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    Neural oscillations can be measured by electroencephalography (EEG) and these oscillations can be characterized by their frequency, amplitude and phase. The mechanistic properties of neural oscillations and their synchronization are able to explain various aspects of many cognitive functions such as motor control, memory, attention, information transfer across brain regions, segmentation of the sensory input and perception (Arnal and Giraud, 2012). The alpha band frequency is the dominant oscillation in the human brain. This oscillatory activity is found in the scalp EEG at frequencies around 8-13 Hz in all healthy adults (Makeig et al., 2002) and considerable interest has been generated in exploring EEG alpha oscillations with regard to their role in cognitive (Klimesch et al., 1993; Hanselmayr et al., 2005), sensorimotor (Birbaumer, 2006; Sauseng et al., 2009) and physiological (Lehmann, 1971; Niedermeyer, 1997; Kiyatkin, 2010) aspects of human life. The ability to voluntarily regulate the alpha amplitude can be learned with neurofeedback training and offers the possibility to control a brain-computer interface (BCI), a muscle independent interaction channel. BCI research is predominantly focused on the signal processing, the classification and the algorithms necessary to translate brain signals into control commands than on the person interacting with the technical system. The end-user must be properly trained to be able to successfully use the BCI and factors such as task instructions, training, and especially feedback can therefore play an important role in learning to control a BCI (Neumann and Kübler, 2003; Pfurtscheller et al., 2006, 2007; Allison and Neuper, 2010; Friedrich et al., 2012; Kaufmann et al., 2013; Lotte et al., 2013). The main purpose of this thesis was to investigate how end-users can efficiently be trained to perform alpha band modulation recorded over their sensorimotor cortex. The herein presented work comprises three studies with healthy participants and participants with schizophrenia focusing on the effects of feedback and training time on cortical activation patterns and performance. In the first study, the application of a realistic visual feedback to support end-users in developing a concrete feeling of kinesthetic motor imagery was tested in 2D and 3D visualization modality during a single training session. Participants were able to elicit the typical event-related desynchronisation responses over sensorimotor cortex in both conditions but the most significant decrease in the alpha band power was obtained following the three-dimensional realistic visualization. The second study strengthen the hypothesis that an enriched visual feedback with information about the quality of the input signal supports an easier approach for motor imagery based BCI control and can help to enhance performance. Significantly better performance levels were measurable during five online training sessions in the groups with enriched feedback as compared to a conventional simple visual feedback group, without significant differences in performance between the unimodal (visual) and multimodal (auditory–visual) feedback modality. Furthermore, the last study, in which people with schizophrenia participated in multiple sessions with simple feedback, demonstrated that these patients can learn to voluntarily regulate their alpha band. Compared to the healthy group they required longer training times and could not achieve performance levels as high as the control group. Nonetheless, alpha neurofeedback training lead to a constant increase of the alpha resting power across all 20 training session. To date only little is known about the effects of feedback and training time on BCI performance and cortical activation patterns. The presented work contributes to the evidence that healthy individuals can benefit from enriched feedback: A realistic presentation can support participants in getting a concrete feeling of motor imagery and enriched feedback, which instructs participants about the quality of their input signal can give support while learning to control the BCI. This thesis demonstrates that people with schizophrenia can learn to gain control of their alpha oscillations recorded over the sensorimotor cortex when participating in sufficient training sessions. In conclusion, this thesis improved current motor imagery BCI feedback protocols and enhanced our understanding of the interplay between feedback and BCI performance.Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Corte
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