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This paper deals with an acquisition and recognition of Electromyography (EMG) signals, their processing to get the neural activation, their transformation to muscle activation and the relationship of neural activation to muscle activation. This issue is related to the stability of an EMG-based orthotic control. The EMG signals are recorded with surface electrodes attached to the skin on top of the...
A group of the neurons in the brain responds to the stimuli from the outside and inside. The information propagates to a suitable area and is processed in the correct way in the brain of a healthy person. When a series of processes has some problems, a psychoneurotic disorder can be caused. The objective of this research is to visualize the correlation and propagation of the information between the...
This work presents that, only using non-invasively captured brain signals, a person can navigate an electric wheelchair with no serious training for a long term. Only two electrodes are set on the scalp non-invasively to detect a P300 EEG signal and a reference signal. A simple signal processing interprets the measured signals to decide a movement direction of the wheelchair. The whole devices are...
The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses...
Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation...
This paper develops a method to determine the minimum duration interval which ensures that the process of “sorting” the extracellular action potentials recorded during that interval achieves a desired confidence level of accuracy. During the recording process, a sequential decision theory approach continually evaluates a variant of the likelihood ratio test using the model evidence of the sorting/clustering...
This paper presents our experience with developing a portable wireless medical sensor device. We use National Instruments (NI) devices and LabView for measurements studying fatigue of patients suffering multiple sclerosis (MS). Fatigue is a very frequent symptom perceived by MS patients, but the disease mechanism is poorly understood. Many efforts have been made to increase the understanding of this...
This paper presents a comparative study over the detection of Steady-State Visual Evoked Potential (SSVEP) with monopolar or bipolar electroencephalographic (EEG) recordings in a Brain-Computer Interface experiment. Five subjects participated in this study. They were stimulated with four flickering lights at 13, 14, 15 and 16 Hz and the EEG was measured simultaneously with two bipolar channels (O1...
We compare the results given by different methods to reconstruct cortical sources activity in order to classify EEG in real time. Two motor imagery experiments were performed. The aim was to retrieve from 1-second windows of signal which motor imagery task the subjects were performing. The use of cortical activity reconstruction was compared to Laplacian filtering, which is often used in BCI. A recursive...
In this paper, we present an analysis of magnetoencephalography (MEG) signals from a patient with whole-body chronic pain in order to investigate changes in neural activity induced by DBS. The patient is one of the few cases treated using DBS of the anterior cingulate cortex (ACC). Using MEG to reconstruct the neural activity of interest is challenging because of interference to the signal from the...
Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents...
Remote surveillance is important for patients with atrial fibrillation (AF). Atrial signal recognition with conventional monitoring devices is difficult; remote AF detection is predominantly accomplished by R-R interval analysis. Twelve lead ECG (12L) displays atrial activity and remains the gold standard for AF diagnosis. CardioBip is a portable wireless patient-activated event monitor providing...
Combining non-invasive monitoring of action-related brain signals with the invasive recordings of the nerve motor output could provide robust natural and bidirectional multimodal Brain-Machine interfaces. One 26 years old, right-handed male who had suffered traumatic trans-radial amputation of the left arm was connected in a bidirectional way with a robotic hand prostheses. Cortical signals related...
This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface...
In this work, we implemented a brain-machine interface (BMI) based on electroencephalographic (EEG) signals and used it to classify and separate three types of mental tasks: motor imagery with the right and left hands and simple arithmetic sums. In order to reduce dimension of variables and increase classification power, we used both PCA and ICA based algorithms for spectral analysis. Our results...
The functional connectivity of the brain is investigated through the study of multivariate autoregressive models (MVAR) applied to multichannel EEG recordings. After the identification of the model, different indices can be calculated that are able to quantify direct and indirect functional connections between cortical areas. These methodology is used for the investigation of possible connectivity...
A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes...
Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention...
To evaluate sleep quality or autonomic nervous system, many annoying electrodes have be attached to subjects' body. It can disturb comfortable sleep and, moreover, since it is very expensive experiment, continuous sleep monitoring is difficult. Since heart rate reflects the autonomic nervous system, it is highly synchronized with the sympathetic activation during transition from non-REM sleep to wakefulness...
The present study investigates the behavior of action potential conduction velocity (CV) on each repetition of an isokinetic test set and on each set as a whole. A total of seven healthy men (27.7 ± 2.8 yrs, 1.74 ± 0.06 m, and 79.6 ± 11.0 Kg) performed 3 (three) sets of 10 (ten) maximal concentric repetitions of dominant knee extension at 60°/s on an isokinetic dynamometer, with 1 minute of rest interval...
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