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.

Brain Machine Interface (BMI) and Software Agent (SA) can provide some new adaptive strategies for robust BMI implementations. In this work, a non-invasive Adaptive BMI is introduced, which has been designed to discriminate four mental tasks. The SA allows tracking features to contribute for an adaptive process, while the user's engagement state provides a feedback between BMI and the environment. The Silhouette's width is the performance measurement used for the active learning process. The results show that the implemented system allows high accuracy (75%) in the classification process.

Active learning for adaptive brain machine interface based on Software Agent

In this paper is presented an experiment designed to detect the will to perform several steps forward (as walking onset) before it occurs using the electroencephalographic (EEG) signals collected from the scalp. The preliminary results from five users have been presented. In order to improve the quality of the signals acquired some different spatial filters are applied and compared. In the future, the improved Brain-Computer Interface of this paper will be used as part of the control system of an exoskeleton attached to the lower limb of people with incomplete and complete spinal cord injury to initiate their gait cycle.

Preliminary Study to Detect Gait Initiation Intention Through a BCI System

The combination of rehabilitation technologies and better control systems for the assistive technologies allows us to create brain-machine interfaces for motion control by using exoskeleton devices. In this work, the creation of a protocol and analysis method is developed for human decision-making decoding in tasks related to motor activity. The aims of this research is to be able to develop a real-time interface for the control of a lower-limb exoskeleton in the future. The proposed analysis tries to decode the patterns when subjects have the intention to change their current speed on their own will. 4 subjects performed the experiments obtaining results around 65%.

Eduardo Iáñez

Papers

Active learning for adaptive brain machine interface based on Software Agent

Preliminary Study to Detect Gait Initiation Intention Through a BCI System

Analysis of frequency bands and channels configuration for detecting intention of change speed through EEG