Student, Universidad de las Américas Puebla
Present the peak alpha frequency as a standard for predicting the number of sessions of neurofeedback the patient needs in order to have an improvement in its cognitive ability.
Abstract: While issues of efficacy and specificity are crucial for the future of neurofeedback training, there may be alternative designs and control analyses to circumvent the methodological and ethical problems associated with double-blind placebo studies. Surprisingly, most NF studies do not report the most immediate result of their NF training, i.e., whether or not children with ADHD gain control over their brain activity during the training sessions. For the investigation of specificity, however, it seems essential to analyze the learning and adaptation processes that take place in the course of the training and to relate improvements in self-regulated brain activity across training sessions to behavioral, neuropsychological and electrophysiological outcomes. To this aim, a review of studies on neurofeedback training with ADHD patients which include the analysis of learning across training sessions or relate training performance to outcome is presented. Methods on how to evaluate and quantify learning of EEG regulation over time are discussed. "Non-learning" has been reported in a small number of ADHD-studies, but has not been a focus of general methodological discussion so far. For this reason, selected results from the brain-computer interface (BCI) research on the so-called "brain-computer illiteracy", the inability to gain control over one's brain activity, are also included. It is concluded that in the discussion on specificity, more attention should be devoted to the analysis of EEG regulation performance in the course of the training and its impact on clinical outcome. It is necessary to improve the knowledge on characteristic cross-session and within-session learning trajectories in ADHD and to provide the best conditions for learning.
Pub.: 15 Apr '15, Pinned: 29 Aug '17
Abstract: Commercially available electroencephalogram devices suitable for brain computer interface research are now widely available for neurofeedback applications.The authors of this study were interested in exploring the usability and acceptance of a commercially available electroencephalogram as a first step in introducing the technology, assessing patient receptivity, and acquiring preliminary clinical outcome data.The study was conducted among active duty military service members referred for psychiatric treatment to the Walter Reed National Military Medical Center's Psychiatry Continuity Service in Bethesda, MD. The investigators used a commercially available single channel dry electrode electroencephalogram device paired with software programs that focused on promoting mediation and attention. A satisfaction survey was completed at the completion of each session.One hundred and one (101) military patients completed a total of 273 brain computer interface sessions from May 2012 through June 2014. Participants overwhelmingly found the single channel electroencephalogram device easy to use (n=265/271, 97.8%). Following completion of the session participants most frequently reported "more focus" (n=85/271, 31.4%) followed by "more relaxed" (n=71/271, 26.2%), and "a sense of accomplishment" (n=44/271, 16.2%).Based on survey results gleaned from 273 sessions completed during the two year study, brain computer interface using a single channel electroencephalogram was overwhelming rated as user friendly. Over two-thirds of the individual sessions were rated as improving the person's focus, relaxation, or sense of accomplishment.
Pub.: 18 Apr '15, Pinned: 29 Aug '17
Abstract: Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.
Pub.: 09 Jul '17, Pinned: 29 Aug '17
Abstract: We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L), 2) selective sensation on right hand (SS-R), 3) bilateral selective sensation (SS-B), and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9±8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3±4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had accuracy of 75.2±8.3%, with the best discrimination reached for the classes SS-L, SS-R and SS-S. Finally, four classes were classified with accuracy of 59.4±7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.
Pub.: 26 Jul '17, Pinned: 29 Aug '17