The system of electric brain activity acquisition from EEG equipment for Linux OS

The idea of electric brain activity acquisition for LINUX OS is presented. So far the only available software has been that for MS-Windows. The general concept of the modular data acquisition system as independent of its particular parts as possible and based on the LINUX pipe system will be discussed to some extent. Adapting the hardware to open source technology will allow us to design new methods of brain electrical dynamic analysis.

Developing brain electric activity acquisition software for Linux

This article discusses the first successful phase of our work on the construction of a complete modular research station for EEG signal acquisition and analysis performed in real-time mode and in a way that meets our needs. Our intentions were presented in [1]

Synchronous SSVEP Data Acquisition System

Steady State Visually Evoked Potentials have been known for several decades and theyappear in the primary visual cortex of brain as a result of light stimulation of the sense of sight. Inthis article a simple method for electroencephalographic data acquisition is presented. The system isbased on the DSM-51 unit connected to goggles with blinking diodes and Mindset-1000 EEG amplifierwith 16 channels. We present self-developed hardware and method of effective synchronization for thelight stimulation and brain activity recording

A feasibility study of a complete low-cost consumer-grade brain-computer interface system

Brain-computer interfaces (BCIs) are technologies that provide the user with an alternative way of communication. A BCI measures brain activity (e.g. EEG) and converts it into output commands. Motor imagery (MI), the mental simulation of movements, can be used as a BCI paradigm, where the movement intention of the user can be translated into a real movement, helping patients in motor recovery rehabilitation. One of the main limitations for the broad use of such devices is the high cost associated with the high-quality equipment used for capturing the biomedical signals. Different low-cost consumer-grade alternatives have emerged with the objective of bringing these systems closer to the final users. The quality of the signals obtained with such equipments has already been evaluated and found to be competitive with those obtained with well-known clinical-grade devices. However, how these consumer-grade technologies can be integrated and used for practical MI-BCIs has not yet been explored. In this work, we provide a detailed description of the advantages and disadvantages of using OpenBCI boards, low-cost sensors and open-source software for constructing an entirely consumer-grade MI-BCI system. An analysis of the quality of the signals acquired and the MI detection ability is performed. Even though communication between the computer and the OpenBCI board is not always stable and the signal quality is sometimes affected by ambient noise, we find that by means of a filter-bank based method, similar classification performances can be achieved with an MI-BCI built under low-cost consumer-grade devices as compared to when clinical-grade systems are used. By means of this work we share with the BCI community our experience on working with emerging low-cost technologies, providing evidence that an entirely low-cost MI-BCI can be built. We believe that if communication stability and artifact rejection are improved, these technologies will become a valuable alternative to clinical-grade devices.Fil: Peterson, Victoria. Universidad Nacional de Entre Ríos; ArgentinaFil: Galván, Catalina María. Universidad Nacional del Litoral; ArgentinaFil: Hernández, Hugo. Universidad Nacional de Entre Ríos; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentin

Localization of sources in electroencephalographic registers during working memory tasks

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director: Albert Compte. Tutor: Santiago Marco.EEG source localization is a non-invasive imaging technique developed to locate the anatomical sources recorded at the scalp during an EEG recording. The reconstruction of the sources can be computed by solving the so-called inverse problem. This is an ill-posed problem which aims in estimating the sources that fit the recorded measurements. There exist several powerful commercial and academic software packages that cover multiple methods on data processing, source localization, and statistical analysis. In this work, the open-source MNE-Python package was selected as the working environment used to address the challenge of characterizing and locating neural activation. This study provides a pipeline with practical steps on the EEG source localization technique. The results obtained in this project have been validated by experts in the Theoretical Neurobiology and Computational Neuroscience fields In this project, the EEG source localization has been computed over a group of encephalitic patients and a control group. The two groups had shown differences regarding the electrical activity in a working memory trial. This study aimed in localizing the anatomical brain regions that were responsible of the electrical differences. It has been observed that instants before the stimulus, the activated sites between control groups and encephalitic groups differ. In the case of the control group, the activated region was located at the frontal lobe of the left hemisphere. Whereas, in the case of the encephalitic group the activated region was located at the temporal lobe of the right hemisphere

NON INVASIVE INVESTIGATION OF SENSORIMOTOR CONTROL FOR FUTURE DEVELOPMENT OF BRAIN-MACHINE-INTERFACE (BMI)

My thesis focuses on describing novel functional connectivity properties of the sensorimotor system that are of potential interest in the field of brain-machine interface. In particular, I have investigated how the connectivity changes as a consequence of either pathologic conditions or spontaneous fluctuations of the brain's internal state. An ad-hoc electronic device has been developed to implement the appropriate experimental settings. First, the functional communication among sensorimotor primary nodes was investigated in multiple sclerosis patients afflicted by persistent fatigue. I selected this condition, for which there is no effective pharmacological treatment, since existing literature links this type of fatigue to the motor control system. In this study, electroencephalographic (EEG) and electromyographic (EMG) traces were acquired together with the pressure exerted on a bulb during an isometric hand grip. The results showed a higher frequency connection between central and peripheral nervous systems (CMC) and an overcorrection of the exerted movement in fatigued multiple sclerosis patients. In fact, even though any fatigue-dependent brain and muscular oscillatory activity alterations were absent, their connectivity worked at higher frequencies as fatigue increased, explaining 67% of the fatigue scale (MFIS) variance (p=.002). In other terms, the functional communication within the central-peripheral nervous systems, namely involving primary sensorimotor areas, was sensitive to tiny alterations in neural connectivity leading to fatigue, well before the appearance of impairments in single nodes of the network. The second study was about connectivity intended as propagation of information and studied in dependence on spontaneous fluctuations of the sensorimotor system triggered by an external stimulus. Knowledge of the propagation mechanisms and of their changes is essential to extract significant information from single trials. The EEG traces were acquired during transcranial magnetic stimulation (TMS) to yield to a deeper knowledge about the response to an external stimulation while the cortico-spinal system passes through different states. The results showed that spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. As probed by TMS, this behavior indicates that the effective connectivity within bilateral circuits orchestrating hand control are dynamically modulated in time even in resting state. The third investigation assessed the plastic changes in the sensorimotor system after stroke induced by 3 months of robotic rehabilitation in chronic phase. A functional source extraction procedure was applied on the acquired EEG data, enabling the investigation of the functional connectivity between homologous areas in the resting state. The most significant result was that the clinical ameliorations were associated to a ‘normalization’ of the functional connectivity between homologous areas. In fact, the brain connectivity did not necessarily increase or decrease, but it settled within a ‘physiological’ range of connectivity. These studies strengthen our knowledge about the behavioral role of the functional connectivity among neuronal networks’ nodes, which will be essential in future developments of enhanced rehabilitative interventions, including brain-machine interfaces. The presented research also moves the definition of new indices of clinical state evaluation relevant for compensating interventions, a step forward

Brain and Human Body Modeling 2020

​This open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical

EEG based volitional interaction with a robot to dynamically replan trajectories

Everyday robots are more involved in our daily basis and they are expected to be part of the domestic environment eventually. Assertive robots deal with this scenario providing help to people with certain disabilities, a task that requires an intuitive communication between human and robot. One of the control methods that have become popular is the Brain Computer Interfaces (BCI), using electroencephalography (EEG) signals to read the user’s intention. Commonly applied to let the user choose among several options, without further interaction once the robot starts acting. This project explain a method to interpret the EEG signal online and use it to manipulate online the movement of a robot arm. The signal that is received comes from Motion Imagery to allow the user to communicate constantly his intention. Using Dynamic Movement Primitives (DMP) and Virtual Force Systems the stored trajectories of the robot can be modified online trying to adapt to the user’s will. With this elements the trajectories are applied in a real robot arm, chequing online if all the requested goals are feasible positions for the robot. This method intend to make more natural the collaboration with a robot in domestic tasks, where slight modifications of an action can lead into a more satisfactory interaction