Data Analysis
Research Papers
Interacting brains coming in sync through their minds: an interbrain neurofeedback study
Neurophysiological evidence shows that interpersonal action coordination is accompanied by interbrain synchronization (IBS). However, the functional significance of this association remains unclear. Using two experimental designs, we explored whether IBS is amenable to neurofeedback (NFB). Feedback was provided either as two balls approaching each other (so-called ball design), or as two pendula, each reflecting the oscillatory activity of one of the two participants (so-called pendulum design). The NFB was provided at delta (i.e., 2.5 Hz) and theta (i.e., 5 Hz) electroencephalography frequencies, and manipulated by enhanced and inverse feedback. We showed that the participants were able to increase IBS by using NFB, especially when it was fed back at the theta frequency. Apart from intra- and interbrain coupling, other oscillatory activities (e.g., power spectral density, peak amplitude, and peak frequency) also changed during the task compared with the rest. Moreover, all the measures showed specific correlations with the subjective postsurvey item scores, reflecting subjective feeling and appraisal. We conclude that the use of IBS for NFB might help in specifying the contribution of IBS to interpersonal action coordination and in providing important information about the neural mechanisms of social interaction and the causal dimension of IBS.
View Full Paper →Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).
View Full Paper →Standards for the Use of Quantitative Electroencephalography (QEEG) in Neurofeedback: A Position Paper of the International Society for Neuronal Regulation
Background. This paper presents the findings of an interdisciplinary committee on standards for quantitative electroencephalography (QEEG) in neurofeedback which has been unanimously accepted by the International Society for Neuronal Regulation (ISNR) Board as a position paper of ISNR. Method. The committee reviewed current standards for quantitative encephalography in other specialties as well as scholarly literature on QEEG. Results. The panel reached the following conclusions: Although clinical research indicates that a full 19 channel QEEG does not appear necessary for conducting successful neurofeedback training, an increasing number of clinicians are using comprehensive QEEG evaluations to guide their neurofeedback training. An impressive body of peer reviewed scientific literature attests to the utility of the QEEG in providing a scientifically objective and clinically practical assessment of a wide range of psychiatric, psychological and medical conditions. Many of the significant contributions to the field of QEEG have come from psychologists and the Board of Professional Affairs of the American Psychological Association has concluded that QEEG is within the scope of practice of psychologists trained in this specialty. Unlike neurology and psychiatry, where QEEG is principally used for purposes of diagnosing medical pathology, neurotherapists who use QEEG primarily do so to guide EEG biofeedback training. It is not necessary for a physician to screen raw EEG data as part of a QEEG evaluation for neurofeedback training. Conclusions. For the purpose of encouraging high standards, recommendations are made for areas of training and study in this specialty, for certification, for equipment/software, and for procedures in data collection and analysis.
View Full Paper →Learning to control brain rhythms: making a brain-computer interface possible
The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time.
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