In studies involving patients with special needs, the use of electroencephalography (EEG) recordings is among the most delicate measurement modalities. The quietness needed and the long preparation time can be challenging especially in young ages. Furthermore, the invasive appearance of the instrumentation involved is not appealing and can raise distrust in patients. We developed a customized EEG device which adresses these issues by merging commercially available EEG hardware with an unobtrusive headphones design. The resulting device has very short preparation times, non-clinical appearance, and delivers adequate data quality with respect to recording of sensorimotor rhythms. Our device was employed in a study investigating sensorimotorrelated brain activity in adolescents and adults with cerebral palsy (CP) conducted at a day-care center. Experimenters reported convenient data collection and overall acceptance of the system among patients. The changes in sensorimotor rhythms over time during a hand motor task meet the observations described in the literature, supporting the functionality of our EEG device for the assessment of sensorimotor-related measures of brain activity in patients with sensorimotor disorders of neuronal origin.
Alertness level evaluation has obvious implications for safety–critical occupations such as operators in control rooms or drivers. It has already been stated that alertness can be assessed objectively by EEG. However, the high costs of standard medical equipment for EEG measurement, their complex and timeconsuming operation, and the need to use conductive gel on the scalp make this method impossible for general use or at workstations. The aim of the study was to analyze the possibility of alertness level assessment based on EEG measurements using the Emotiv EPOC headset, which is relatively cheap, wireless, comfortable for wearing and does not need the use the of conductive gel, but allows the capture of only 14 channels of EEG. The experiments were carried out in laboratory conditions using three different light spectra for 40 minutes exposure on office workstation during the afternoon drop in alertness. 50 participants took part in each light scene (white, red, blue). The EEG measurements were performed before exposure and just after exposure to a particular light scene. A new measure of alertness, based on analysis of EEG signals, has been introduced. The results showed that this new measure based on low-cost Emotiv EPOC EEG measurements is reliable and confirms the results of previous studies.
A Brain Computer Interface (BCI) is a useful instrument to support human communication, frequently implemented by using electroencephalography (EEG). Regarding the used communication paradigm, a very large number of strategies exist and, recently, self-induced emotions have been introduced. However, in general the actual emotion-based BCIs are just binary, since they are capable of recognizing just a single emotion. A crucial node is the introduction of more than a single emotional state for improving the efficiency of a BCI. In order to be used in BCIs, signals from different emotional states have to be collected, recognized and classified. In the present paper, a method for mapping several emotional states was described and tested on EEG signals collected from a publicly available dataset for emotion analysis using physiological signals (DEAP). The proposed method, its experimental protocol, and preliminary numerical results on three different emotional states were presented and discussed. The method, based on multiple binary classification, was capable of optimizing the most discriminative channels and the features combination for each emotional state and of recognizing between several emotional states through a polling system.
The investigation of the diagnostic possibilities for the arterial hypertension is presented. The 41 features of the statistical, geometric, spectral and nonlinear methods during functional loads were considered for two groups: healthy volunteers and patients suffering from the arterial hypertension of the II-III degree. Application of the linear and quadratic discriminant analysis showed particular features that have high classification efficiency.
Actuality verifying the effectiveness of the dynamic correction of the sympathetic nervous system using a non-invasive multichannel neurostimulation device "SYMPATHOCOR-01". The task is to restore cognitive function in patients with organic amnestic syndrome. Three patients with clinical organic amnestic syndrome resulting of brain damage (poisoning, alcohol and trauma) held inpatient treatment for at least 12 months in the neurology or psychiatry department without a significantly improvement. As a result, the stimulation rate during the three weeks of recovery could achieve stable fixing of memory and other cognitive functions in all the patients according to clinical observation and methods of neuropsychological assessments FAB, MoCA and MMSE. A significant positive trend noted by the results of the data analysis of EEG and heart rate variability. A hypothesis mechanism for clinical effect is formulated. A serious clinical trial should be done to confirm hypothesis.
Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer interface that allows simulating “mouse” clicks by separate gestures and provides proportional control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither special measurement devices nor specific knowledge from the end-user. Thus, the approach enables building of low-budget user-friendly sEMG solutions.
The aim of this study was to investigate the feasibility of depositing a thin layer of boron-doped nanocrystalline diamond (B-NCD) on titanium nitride (TiN) coated electrodes and the effect this has on charge injection properties. The charge storage capacity increased by applying the B-NCD film, due to the wide potential window typical for B-NCD. The impedance magnitude was higher and the pulsing capacitance lower for B-NCD compared to TiN. Due to the wide potential window, however, a higher amount of charge can be injected without reaching unsafe potentials with the B-NCD coating. The production parameters for TiN and B-NCD are critical, as they influence the pore resistance and thereby the surface area available for pulsing.
After stroke, many patients experience hemiparesis or weakness on one side of the body.In order to compensate for this lack of motor function, they tend to overuse their non-affected limb.This so called learned non-use may be one of the most relevant contributors to functional loss after post-stroke hospital discharge.We hypothesize that frequent exposure to movement related feedback through a wearable bracelet device may 1) increase the patient's intrinsic motivation for using the paretic limb, and 2) counteract learned non-use, therefore inducing motor recovery.First, to validate the accelerometers-based measurement of arm use, we recruited 10 right-handed volunteers without neurological impairments.Second, we explored the acceptability and clinical impact of a low-cost wearable system on 4 chronic stroke patients with hemiparesis.Our results suggest that frequent exposure to direct feedback about arm use promotes the integration of the paretic limb in the performance of instrumental activities of daily living (iADLs).In addition, results from questionnaires revealed that the use of wearable devices may influence positively the patient's intrinsic motivation for using the affected arm.To the best of our knowledge, this is the first study suggesting the benefits of wearable-based feedback as an intervention tool for counteracting learned non-use.
The xDAWN algorithm is a well-established spatial filter which was developed to enhance the signal quality of brain-computer interfaces for the detection of event-related potentials. Recently, an adaptive version has been introduced. Here, we present an improved version that incorporates regularization to reduce the influence of noise and avoid overfitting. We show that regularization improves the performance significantly for up to 4%, when little data is available as it is the case when the brain-computer interface should be used without or with a very short prior calibration session.
It is often the case that practical applications of support vector machines (SVMs) require the capability to perform online learning under limited availability of computational resources. Enabling SVMs for online learning can be done through several strategies. One group thereof manipulates the training data and limits its size. We aim to summarize these existing approaches and compare them, firstly, on several synthetic datasets with different shifts and, secondly, on electroencephalographic (EEG) data. During the manipulation, class imbalance can occur across the training data and it might even happen that all samples of one class are removed. In order to deal with this potential issue, we suggest and compare three balancing criteria. Results show, that there is a complex interaction between the different groups of selection criteria, which can be combined arbitrarily. For different data shifts, different criteria are appropriate. Adding all samples to the pool of considered samples performs usually significantly worse than other criteria. Balancing the data is helpful for EEG data. For the synthetic data, balancing criteria were mostly relevant when the other criteria were not
The power of oscillatory components of the electroencephalogram (EEG) can be predictive for the single-trial performance score of an upcoming task. State-of-the-art machine learning methods allow to extract such predictive subspace components even from noisy multichannel EEG recordings. In the context of an isometric hand motor rehabilitation task, we analyse EEG data of n=20 normally aged subjects. Predictive oscillatory EEG subspaces were derived with a spatial filtering method (source power comodulation, SPoC), and the transfer of these subspaces between five performance metrics but within data of single subjects was investigated. Findings suggest, that on the grand average of 20 subjects, informative SPoC subspace components were extracted, which could be shared between a set of three metrics describing the duration of subtasks and jerk characteristics of the force trajectories. Transfer to any other of the remaining four metrics was not possible above chance level for a metric describing the reaction time and a metric assessing the length of the force trajectory. Furthermore we show, that these transfer results are in line with the structure of cross-correlations between the performance metrics.
This paper describes the design and development of a web interface used for an analysis of neural activities of the Caenorhabditis elegans (C. elegans) nematode, within the framework of the Si elegans project. The Si elegans project develops a platform, where the neural system of C. elegans is emulated in hardware and the physical worm together with the external environment is simulated in software. This platform allows for virtual execution of a variety of behavioural experiments of C. elegans. We use the herein described web interface to post-experimentally visualize the neural activity as well as the worm’s behaviour and allow for its deeper analysis. The web-page joins a 3D virtual environment with the 2D GUI in order to realistically visualize the worm and the emulated neural processes, along with additional configuration information. In the virtual environment, the locomotion of the worm is shown, including the motion of neurons. Visualizing the location of the neurons, the user can understand signal transmission among the neurons in a more intuitive way. In the 2D part, additional information about the neurons is displayed. Mainly, a grid of buttons that shows the actual spiking process of the neurons by colour changes and neuron specific voltage graphs following the potential evolution of selected neurons. We believe that this approach suits the exploration of small neuronal circuits, like is the ones of C. elegans.
Affective Computing and Brain Computer Interface (BCI) are two innovative and rapidly growing fields of research. Affective Computing aims at equipping machines with the human capabilities of observe, understand and express affecting features; BCI aims at discovering novel communication channels and protocols, through the monitoring of the brain activity. Emotion recognition plays a central role in both these research fields. In this work we present an EEG poll based classification algorithm for self-induced emotional states used for BCI. We tested the approach using three emotions: the disgust produced by remembering an unpleasant odor (a stink), the pleasantness induced by the memory of a fragrance and a relaxing state. Preliminary experimental results are also reported.
