Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program

The activities are reported of the NASA Biomedical Applications Team at Southwest Research Institute between 25 August, 1972 and 15 November, 1973. The program background and methodology are discussed along with the technology applications, and biomedical community impacts

Southwest Research Institute assistance to NASA in biomedical areas of the Technology Utilization program

Technology utilization in biomedical areas, particularly for infants and handicapped person

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention

This paper describes a hardware/software platform to acquire human body signals. In the field of physiological computing it is desirable to have a system that allows the synchronized acquisition of signals coming from different sources. Here is described how to unify the whole process of acquiring signals from both customized hardware and low cost commercial devices such as Neurosky’s mindwave. A case study using this platform is also shown: studying the feasibility of using sustained attention to access a computer. In order to do that brain activity was measured using Neurosky’s mindwave. The participants in this study were asked to keep their attention high/low for as long as possible during several trials. Experimentation was performed by 7 normally developed subjects and 3 people with cerebral palsy (CP). Our preliminary work shows that 60% of participants might be potential users of this technology. Eventually, modulating the attention to access a communication board needs a scanning period greater than 5.76s

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program Final report, 1 Feb. 1969 - 24 Aug. 1970

Research progress in technology transfer by NASA Biomedical Application Tea

A Panoramic Study of Obstructive Sleep Apnea Detection Technologies

This study offers a literature research reference value for bioengineers and practitioner medical doctors. It could reduce research time and improve medical service efficiency regarding Obstructive Sleep Apnea (OSA) detection systems. Much of the past and the current apnea research, the vital signals features and parameters of the SA automatic detection are introduced.The applications for the earlier proposed systems and the related work on real-time and continuous monitoring of OSA and the analysis is given. The study concludes with an assessment of the current technologies highlighting their weaknesses and strengths which can set a roadmap for researchers and clinicians in this rapidly developing field of study

Predicting optimal anesthesia level from propofol and remifentanil concentration: analysis of covariate factors for individualization

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Tutor: Pedro Gambús Cerrillo. Tutor Extern: Sebastián Jaramillo SelmanGeneral anesthesia involves some targeting effects which aim to prevent the patient from suffering against the therapeutic aggression. These effects are hypnosis, analgesia, amnesia and immobility and to achieve them a combination of drugs is delivered into the patient, from which propofol and remifentanil are highlighted. In the operating room, monitoring systems are used to assess the depth of anesthesia in real time. This monitoring includes basic systems such as arterial blood pressure, oxygenation or electrocardiogram and electroencephalogram derived measures, which are more complex; from this last group, BIS index is a good indicator. Being able to predict the anesthetic depth from a set of input variables could be valuable during the surgery, as it would help the anesthesiologists to prevent adverse effects, and it would help the post-operative recovery. Knowing this, the aim of this project is to predict the probability to be in the optimal level of anesthesia, which is related to the BIS index. This probability is obtained from the input concentration of propofol and remifentanil, a hypnotic and an analgesic drug respectively, and from the demographic variables such as age, height or gender. To do so, a Logistic Regression model will be built with data from patients undergoing general anesthesia in Cirurgia Major Ambulatòria (CMA) in Hospital Clínic