Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files

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A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

The Psychology of Fear and Stress, J.A. Gray (Ed.). Cambridge University Press, Cambridge (1987), viii and 422 pp, ISBN 0-521-27098-7

EMG and EOG artifacts in brain computer interface systems: A survey

Across 5 decades, hundreds of randomized trials have tested psychological therapies for youth internalizing (anxiety, depression) and externalizing (misconduct, attention deficit and hyperactivity disorder) disorders and problems. Since the last broad-based youth meta-analysis in 1995, the number of trials has almost tripled and data-analytic methods have been refined. We applied these methods to the expanded study pool (447 studies; 30,431 youths), synthesizing 50 years of findings and identifying implications for research and practice. We assessed overall effect size (ES) and moderator effects using multilevel modeling to address ES dependency that is common, but typically not modeled, in meta-analyses. Mean posttreatment ES was 0.46; the probability that a youth in the treatment condition would fare better than a youth in the control condition was 63%. Effects varied according to multiple moderators, including the problem targeted in treatment: Mean ES at posttreatment was strongest for anxiety (0.61), weakest for depression (0.29), and nonsignificant for multiproblem treatment (0.15). ESs differed across control conditions, with "usual care" emerging as a potent comparison condition, and across informants, highlighting the need to obtain and integrate multiple perspectives on outcome. Effects of therapy type varied by informant; only youth-focused behavioral therapies (including cognitive-behavioral therapy) showed similar and robust effects across youth, parent, and teacher reports. Effects did not differ for Caucasian versus minority samples, but more diverse samples are needed. The findings underscore the benefits of psychological treatments as well as the need for improved therapies and more representative, informative, and rigorous intervention science. (PsycINFO Database Record

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What five decades of research tells us about the effects of youth psychological therapy: A multilevel meta-analysis and implications for science and practice.

Given the high incidence of brain injury in the population, brain damage rehabilitation is still a relatively undeveloped field. Virtual reality (VR) has the potential to assist current rehabilitation techniques in addressing the impairments, disabilities, and handicaps associated with brain damage. The main focus of much of the exploratory research performed to date has been to investigate the use of VR in the assessment of cognitive abilities, but there is now a trend for more studies to encompass rehabilitation training strategies. This review describes studies that have used VR in the assessment and rehabilitation of specific disabilities resulting from brain injury, including executive dysfunction, memory impairments, spatial ability impairments, attention deficits, and unilateral visual neglect. In addition, it describes studies that have used VR to try to offset some of the handicaps that people experience after brain injury. Finally, a table is included which, although not an exhaustive list of everything that has been published, includes many more studies that are relevant to the use of VR in the assessment and rehabilitation of brain damage. The review concludes that the use of VR in brain damage rehabilitation is expanding dramatically and will become an integral part of cognitive assessment and rehabilitation in the future.

Virtual Reality in Brain Damage Rehabilitation: Review

Our main goal in this research was to validate the possibility of virtual reality (VR) for attention enhancement in cognitive training program. Then, we developed some cognitive training tasks using VR technology. Thirty subjects who had little behavioral problems and social problems were randomly assigned into three groups: VR group, non-VR group, and control group. Only the VR group used the head-mounted display (HMD) and position sensor. While the VR group and non-VR group performed cognitive training, the control group received no special treatment. All participants took their eight session tasks over 2 weeks. Participants executed a continuous performance task (CPT) before and after training sessions. We found that immersive VR with cognitive training is effective for attention enhancement. Also, we confirmed that our cognitive training can improve the attention span of children and adolescents with behavioral problems and help them learn to focus on some tasks.

The Effect of Virtual Reality Cognitive Training for Attention Enhancement

In this research, the effectiveness of neurofeedback, along with virtual reality (VR), in reducing the level of inattention and impulsiveness was investigated. Twenty-eight male participants, aged 14-18, with social problems, took part in this study. They were separated into three groups: a control group, a VR group, and a non-VR group. The VR and non-VR groups underwent eight sessions of neurofeedback training over 2 weeks, while the control group just waited during the same period. The VR group used a head-mounted display (HMD) and a head tracker, which let them look around the virtual world. Conversely, the non-VR group used only a computer monitor with a fixed viewpoint. All participants performed a continuous performance task (CPT) before and after the complete training session. The results showed that both the VR and non-VR groups achieved better scores in the CPT after the training session, while the control group showed no significant difference. Compared with the other groups, the VR group presented a tendency to get better results, suggesting that immersive VR is applicable to neurofeedback for the rehabilitation of inattention and impulsiveness.

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Neurofeedback training with virtual reality for inattention and impulsiveness.

Fractal analysis was applied to study the trends of EEG signals in the hypnotic condition. The subjects were 19 psychiatric outpatients. Hypnotizability was measured with the Hypnotic Induction Profile (HIP). Fifty-four sets of EEG data were analyzed by detrended fluctuation analysis (DFA), a well-established fractal analysis technique. The scaling exponents, which are the results of fractal analysis, are reduced toward white noise during the hypnotic condition, which differentiates the hypnotic condition from the waking condition. Further, the decrease in the scaling exponents during hypnosis was solely associated with the eye-roll sign within specific cortical areas (F3, C4, and O1/2) closely related to eye movements and attention. In conclusion, the present study has found that the application of the fractal analysis technique can demonstrate the electrophysiological correlations with hypnotic influence on cerebral activity.

Fractal analysis of EEG in hypnosis and its relationship with hypnotizability.

We present a new method to detect epileptiform activity based on median filter and expert rule base. Firstly, we reject the basic artifacts by applying a median filter to the original EEG wave. In this way, we are able to get the higher detection rate in real time which shows a specific noise pattern like EMG. Secondly, after converting the EEG wave into segment and sequence, we find a epileptiform activity by using the Gotman method. Then with expert rule system, we decrease detection error and validate epileptiform activity. The results show better efficiency of artifact rejection. Also, the detection algorithm makes many detection results because of lowering the threshold to find real epileptiform wave. In clinical results, our expert rule system is capable of rejecting artifacts commonly found in EEG recordings. By using the expert rule system, detection rate is found to be higher and shows the capability of real time detection.

Saebyul Kim

Papers

The Effect of Virtual Reality Cognitive Training for Attention Enhancement

Neurofeedback training with virtual reality for inattention and impulsiveness.

Fractal analysis of EEG in hypnosis and its relationship with hypnotizability.

Automatic detection of epileptiform activity using median filter and expert rule base