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Electroencephalography (EEG) signals have been an attractive choice to build noninvasive brain computer interfaces (BCIs) for nearly three decades. Depending on the stimuli, there are different responses which one could get from EEG signals. One of them is the P300 response which is a visually evoked response that has been widely studied. Steady state visually evoked potential (SSVEP) is the response to an oscillating stimulus with fixed frequency, which is detectable from the visual cortex. However, there exists some work on using an m-sequence with different lags as the control sequence of the flickering stimuli. In this study, we used several m-sequences instead of one with the intent of increasing the number of possible command options in a BCI setting. We also tested two different classifiers to decide between the m-sequences and study the performance of multi channel classifiers versus single channel classifiers. The study is done over two different flickering frequencies, 15 and 30 Hz to investigate the effect of flickering frequency on the accuracy of the classification methods. Our study shows that the EEG channels are correlated, and although all the channels contain some useful information, but combining them with a multi channel classifier based on the assumption of having conditional independence will not improve the classification accuracy. In addition, we were able to get reasonably good results using the 30 Hz flickering frequency comparing with 15 Hz flickering frequency that will give us the ability of having a shorter training and decision making time. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 139-147, 2011 [ABSTRACT FROM AUTHOR]

Even with state-of-the-art techniques there are individuals whose paralysis prevents them from communicating with others. Brain–Computer-Interfaces (BCI) aim to utilize brain waves to construct a voice for those whose needs remain unmet. In this paper we compare the efficacy of a BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking, among the most popular modalities used. These results, on healthy individuals without paralysis, suggest that while eye tracking works well for some, it does not work well or at all for others; the latter group includes individuals with corrected vision or those who squint their eyes unintentionally while focusing on a task. It is also evident that the performance of the interface is more sensitive to head/body movements when eye tracking is used as the input modality, compared to using c-VEP. Sensitivity to head/body movement could be better in eye tracking systems which are tracking the head or mounted on the face and are designed specifically as assistive devices. The sample interface developed for this assessment has the same reaction time when driven with c-VEP or with eye tracking; approximately 0.5–1 second is needed to make a selection among the four options simultaneously presented. Factors, such as system reaction time and robustness play a crucial role in participant preferences. [ABSTRACT FROM AUTHOR]

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset of characters. However, the typing accuracy and speed can potentially be enhanced with more informed subset selection. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to each iteration of presentation, rather than showing a subset of randomly selected characters, this framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed. Moreover, we conduct online experiments with human participants to study the human-in-the-loop effect on the performance of the proposed framework and the results were consistent with the simulations. [ABSTRACT FROM PUBLISHER]

Objective: Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children. [ABSTRACT FROM AUTHOR]

An abstract of the study "Signal Processing and Machine Learning of EEG and Respiration Changes During Mindfulness Meditation State" by Barry Oken and colleagues is presented.

Background: This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing.Methods: EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation.Results: Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%).Conclusion: Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. [ABSTRACT FROM AUTHOR]