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.

/pdf/noninvasive-electroencephalogram-based-control-of-a-robotic-vp9s2j59wu.pdf

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

Endogenous brain-machine interface based on the correlation of EEG maps

People with severe motor disorders cannot use their arms and/or hands to interact with the environment. In such cases, a human-machine interface can be used to allow these people to perform activities of daily life. Voice recognition would be the best method to control a device devoted to help with these tasks, but this method is very dependent on the background noise and, under certain circumstances, it cannot be used successfully. In this sense, other methods must be included in the system to assist the voice recognition module. In this project, electrooculography (EOG) has been selected due to its stability and robustness. This way, a multimodal system based on EOG and voice recognition has been developed to control a robotic arm. This paper presents the procedures designed to combine both methods to create a multimodal interface useful for disabled people. Tests presented in this document compare the skills of five different users while controlling the robotic arm to perform pick-and-place tasks. Task...

https://journals.sagepub.com/doi/pdf/10.5772/56592

Multimodal System Based on Electrooculography and Voice Recognition to Control a Robot Arm

In this paper, linear regression models will be used to decode individual joint angles from low frequency EEG components. To that end, isotonic flexion/extension knee movements will be analyzed. Particularly, the decoding performance of healthy and incomplete spinal cord injured subjects will be assessed to determine the behavior of this methodology with motor disabled people. When studying cortical activity during walking, the appearance of muscular artifacts severely influences the EEG signals recorded. The analysis of single joint movements should decrease the noise provoked by the gait process itself. Additionally, different time windows prior to the decoded angle will be assessed to obtain a more reliable decoder. The results show that decoding performance is significantly above chance for most of the subjects (both healthy and disabled) and suggests that meaningful information of the movement planning starts around 2.5 seconds prior to the decoded angle.

Single joint movement decoding from EEG in healthy and incomplete spinal cord injured subjects

The past decade has seen the rapid development of upper limb kinematic decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Previous works show that there is a correlation between electroencephalographic signals and position/velocity hand movement parameters. In these studies, it was observed that uniform and linear movements were better decoded. In this paper, a passive robot assistance has been used during non-invasive hand kinematics decoding. To study the influence of movement variability in this decoding, we propose to use this passive robot arm to track a circle that moves on the screen. Brain signals are recorded while performing the movements. The decoding method is based on a linear regression model applied to low frequency signal components. The decoding accuracy has been analyzed by studying the correlation between the reconstructed and original movements. These results have been compared to previous works in which a computer mouse was used to perform the movements. The results show more stable and realistic decoding accuracies when using the planar robot. Also, we show that decoding accuracy is directly correlated to tracking success.

Passive robot assistance in arm movement decoding from EEG signals

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

Eduardo Iáñez

Papers

Endogenous brain-machine interface based on the correlation of EEG maps

Multimodal System Based on Electrooculography and Voice Recognition to Control a Robot Arm

Single joint movement decoding from EEG in healthy and incomplete spinal cord injured subjects

Passive robot assistance in arm movement decoding from EEG signals

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton