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.

In this paper, an architecture that allows the synchronized recording of cerebral, muscular and biomechanical data during lower limb activities has been designed. The synchronization issue has been addressed. The goal is to analyze the relationship between the different signals, first during simple lower limbs movements, then extending the analysis to gait. Five incomplete spinal cord injury patients and four healthy users participated in experiments to validate the architecture. The users were asked to perform simple movements that involve only one or two joints, particularly knee and ankle. Future studies with the recorded data will address several issues, such as creating neuromusculoskeletal models that relate kinematics data with EMG information, improving the decoding of the angles of the lower limb through EEG signals, or analyzing the coherence between the EEG signals and the EMG information.

Experimental architecture for synchronized recordings of cerebral, muscular and biomechanical data during lower limb activities

Esta investigacion ha sido realizada en el marco del proyecto Walk - Control de exoesqueletos
de miembro inferior mediante interfaces cerebro-maquina para asistir a personas con
problemas de marcha (RTI2018-096677-B-I00), financiado por el Ministerio de Ciencia,
Innovacion y Universidades (MCIU), la Agencia Estatal de Investigacion (AEI) y la Union
Europea a traves del Fondo Europeo de Desarrollo Regional (FEDER).

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Estudio preliminar de la detección de cambios de velocidad de la marcha a partir de señales EEG

Brain Machine Interfaces (BMI) combined with lower-limb exoskeletons can assist patients that have difficulties in walking. However, BMI need some calibration to adjust their parameters to each user. This process is time-consuming and can be fatiguing for the users. In this work, the optimal number of recordings needed to adjust a EEG-based BMI to distinguish between MI of gait and rest state has been studied based on three subjects. The results show that the BMI reaches its highest accuracy with 5 recordings.

Optimal Calibration Time for Lower-Limb Brain–Machine Interfaces

Restoring the gait cycle is vital in motor-impaired people. To accomplish this, it is necessary to study the brain signals in different areas. This work analyzes EEG data offline and pseudo-online for different electrode configurations and different processing-time windows to detect the pedaling start initiation. Premotor cortex is the area related to movement intention. Therefore, in this study, the FZ electrode, which is located in this area, was included in the analysis of the electrode configurations, testing whether it plays an important role in the detection of pedaling start intention. Results show that using time before and after the movement onset for processing is preferred. The presence of the FZ electrode seems to be desirable when analyzing data offline, but is not statistically significant when analyzing data pseudo-online. This suggests the FZ electrode could be ignored when analyzing data in real time, since the processing is the same as pseudo-online.

Analyzing electrode configurations to detect intention of pedaling initiation through EEG signals

In this paper a pseudo-online analysis has been performed to detect the reaction of both healthy users and incomplete Spinal Cord Injury patients to obstacle appearance during walking. A new methodology to improve model gas been described. It consist of discardind trials where brain responses to the obstacles does not correspond with the expected. The results suggest that during the experiments performed with patients, distractions or delayed reactions decrease the results. By applying this methodology, results of patients increase more than 40% being similar to the obtained with healthy users.

Eduardo Iáñez

Papers

Experimental architecture for synchronized recordings of cerebral, muscular and biomechanical data during lower limb activities

Estudio preliminar de la detección de cambios de velocidad de la marcha a partir de señales EEG

Optimal Calibration Time for Lower-Limb Brain–Machine Interfaces

Analyzing electrode configurations to detect intention of pedaling initiation through EEG signals

A new upgrading model for detecting the reaction to obstacle appearance during walking using EEG