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.

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.

Brain computer interfaces (BCIs) offer individuals with disabilities an alternative channel of communication and control, hence they have been receiving increasing interest. BCIs can also be useful for healthy individuals in situations limiting their movement or where other computer interaction modalities need to be supplemented. Event-related and steady state visually evoked potentials (SSVEPs) are the top two brain signal types used in developing BCIs that allow the user to make a choice from a discrete set of options, including the selection of commands from a menu for a robot or computer to perform, as well as typing letters, symbols, or icons for communication. Popular BCI speller paradigms, such as the P300 Matrix Speller, RSVP Keyboard ^TM or SSVEP spellers in which the letters on the keyboard display flicker, are sensitive to the font, size and presentation speed. In addition, sensitivity to eye gaze control plays a significant role in usability of most of these keyboards. We present a code-VEP based BCI, utilized in a language model assisted keyboard application. Utilizing a cursor based selection method, stimuli and targets are separated. FlashType ^TM separates visual stimulation from alphabet presentation to achieve performance invariance under presentation variations. Therefore, FlashType ^TM can be used for all languages, including the ones containing symbols and icons. FlashType ^TM , contains a Static Keyboard, a row of Suggested Characters and a row of Predicted Words. FlashType ^TM , by default, uses only one EEG electrode and four stimuli. The system can operate using only one stimulus at a lower selection rate, useful for individuals with limited or no gaze control. This feature is to be explored in future. Replacing letters with text or icons representing commands would allow controlling a computer or robot. In this study, FlashType ^TM has been evaluated by three individuals performing 10 Mastery tasks. In depth experimentation, such as assessing the system with potential end users writing long passages of text, will be done in future.

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.

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.