Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

Transcranial direct current stimulation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links bet...

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

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Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains.

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

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Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.

Con la creciente preocupación social ante el número de personas que sufren de disfunciones motoras, surgen múltiples estudios orientados a la mejora de las terapias de rehabilitación. En este aspecto, ha surgido una rama científica centrada en el desarrollo de interfaces cerebro-máquina que controlan el movimiento de un exoesqueleto durante terapias de rehabilitación. En este trabajo se presenta una interfaz cerebro-máquina capaz de proporcionar a un exoesqueleto el nivel de atención que un paciente presenta durante la marcha. Para evaluar la validez de los resultados de este sistema, se realizan pruebas empleando y no empleando un exoesqueleto. Los resultados muestran una cada considerable en el los aciertos cuando se utiliza el exoesqueleto debido a la aparición de artefactos de movimiento.

Evaluación de artefactos en un Sistem BMI para la medición de niveles de atención durante movimiento con exoesqueleto de miembro inferior

Motor imagery (MI) has been used as a control paradigm of external devices for brain-machine interfaces (BMI). One of the main limitations of EEG based MI-BMI systems is the sensitivity of the performance to changes in the environment, signal properties, and subjects’ state of mind, especially the ability to stay focused and their alertness. This paper presents a new approach for improving the accuracy of a MI-BMI for controlling a lower-limb exoskeleton by adapting the classifier depending on the level of alertness. Results showed that the performance of MI-BMI sightly improves when predictions are modified based on alertness level, especially for subjects that had lower baseline accuracy.

Adapting EEG based MI-BMI depending on alertness level for controlling a lower-limb exoskeleton

espanolEl objetivo principal del presente proyecto es mejorar el diseno y la estrategia de control de un exoesqueleto de mano para una terapia de rehabilitacion. El exoesqueleto, impreso en 3D, apoya al movimiento del usuario cuando este no este realizando correctamente la tarea o no pueda seguir el patron solicitado. Se ha desarrollado una estrategia de control basada en senales electromiograficas (EMG) y la posicion de los servomotores. Ademas, se ha realizado una interfaz grafica que sirve como realimentacion visual al usuario, y permite conocer como ha realizado la terapia. Se han realizado pruebas en tres usuarios, validando de esta forma la estrategia de control basada en EMG para el control del exoesqueleto. Finalmente, se ha implementado una interfaz cerebrocomputador para el control del exoesqueleto. Un usuario ha validado el sistema realizando imaginacion motora de abrir y cerrar la mano alcanzando un 68% de acierto en la realizacion de la tarea en tiempo real mientras el exoesqueleto apoyaba al movimiento. EnglishThe main objective of this project is to improve the design and the control strategy of a hand exoskeleton for a rehabilitation therapy. This exoskeleton, 3D printed, support the movement of a user when they are not performing properly the task or they cannot follow the requested pattern. A control strategy based in electromyographic signals (EMG) and the servomotors´ position has been developed. Moreover, a graphic interface which serves as feedback to the user has been developed, allows to know the performance of the therapy. Three users validate the control strategy based on EMG signals to control the exoskeleton. Finally, a brain-computer interface to control the exoskeleton has been developed. A user validates the system by performing motor imagination of opening-closing the hand reaching a 68% of success rate performing a real time experiment while the exoskeleton supports their movements.

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Exoesqueleto de bajo coste controlado por señales EMG y EEG orientado a terapias de rehabilitación de mano

The design of solid interfaces based on the patterns of brain activity that underlie human decision-making are a field of interest in creating interfaces that allow recover the pathway between the brain and the muscular system to be rectified. In this work, a Brain Machine Interface is presented to detect the user's intention through the differentiation of the EEG signals into two classes according to their temporal and frequency characteristics, comparing different electrodes configurations. The better band for all the configuration seems to be Theta (4–7 Hz) from a centralize sensorimotor area, which obtained 69 and 70% accuracy results for two subjects.

Eduardo Iáñez

Papers

Evaluación de artefactos en un Sistem BMI para la medición de niveles de atención durante movimiento con exoesqueleto de miembro inferior

Adapting EEG based MI-BMI depending on alertness level for controlling a lower-limb exoskeleton

Exoesqueleto de bajo coste controlado por señales EMG y EEG orientado a terapias de rehabilitación de mano

Analysis of Frequency Bands and Channels Configuration for Detecting Intention of Change Direction Through EEG

Decoding Lower-Limbs Kinematics from EEG Signals While Walking with an Exoskeleton