Brain-computer Interface Commands Research Articles

A toolbox for decoding BCI commands based on event-related potentials.

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.

Interface, interaction, and intelligence in generalized brain–computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

The Influence of Using Different Mental Images as BCI Commands on the Quality of Control

Abstract Brain-Computer Interface (BCI) is a modern and progressive technology for controlling robotic devices. They are broadly applicable in various fields of robotics and information technologies, where alternative methods of controlling electronic devices are used (for example, medical robotics, development of virtual reality applications, game industry, etc.). The most common ways to implement BCI control commands are mental images, evoked potentials, and facial expressions. Compared to other methods, mental images are difficult to perform because of the need to perform an intensified mental activity and concentrate strongly on their execution. At the same time, mental images provide the operator with a promising opportunity to control the robot without performing any physical movements (compared to facial expressions usage) and without using additional equipment for stimuli representation (as is the case of evoked potentials). Therefore, it is necessary to conduct research on various aspects of working with mental images, such as the use of various types of mental images, the impact on the work of distractions, the operator’s preliminary training technique, etc., to improve the user experience of using them and the quality of device control via such way of control. In this paper, we consider use mental images of different types as BCI control commands — visual, sound, kinesthetic. Performing of these commands is involving various parts of the brain. The quality of the operator’s work with these types of mental images is evaluated, including the quality of mental images recognition by the BCI, as well as the time spent by the operator on their execution.

A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Multifocal steady-state visual evoked potentials (mfSSVEPs) have been successfully applied to assess visual field loss in glaucoma. However, the potential of mfSSVEPs for command control has not been fully explored yet. It is significant to detect single-trial mfSSVEPs and establish a brain-computer interface (BCI) system. This study designed a stimulating paradigm that contains 32 targets, with each target composed of five fan-shaped flickers in a circle. The five flickers were modulated by five frequencies and formed a five-bit binary encoding system through controlling the ON/OFF state of each flicker. Twelve subjects participated in an offline and an online experiments. Inter-task-related component analysis (iTRCA) combined with a probabilistic model was proposed for target recognition. Notably, the training data needed for calibration corresponded to only six out of the 32 targets. It was found that the increasing number of flickers showed a negative impact on the mfSSVEP signal. The accuracy reached 80.9% ± 11.7% on average with a peak of 95.3% by iTRCA, which was significantly higher than that by a traditional method. The results indicate that the proposed stimulation and algorithm are effective for encoding and decoding BCI commands. Therefore, the mfSSVEP-based BCI enables the augmentation of the BCI instruction set without any burden of collecting extra training data.

On the Brain Computer Robot Interface (BCRI) to Control Robots

Brain Computer Interfaces (BCI) have emerged as a solid, though promising yet incipient, mechanism to communicate simple and abstract high-level electroencephalography (EEG) commands by decoding brain rhythms. When BCI are used for online commands of a kinematic or dynamic external agent, either real or virtual ones, human perceives the task with different attributes, thus different brain regions are activated depending on the characteristic of the perceptual task and the cognitive process involved. Hence, there arises the question on how the perceived kinematic or dynamic task attributes affects the ability of human to produce a BCI command. In this paper, a Brain Computer Robot Interface (BCRI) is proposed, which contains as a core element a BCI to decode commands for a dynamical external agent, such as a robot. To convey the attributes of a dynamic perceptual task, advanced tools of dynamics and control of robots are borrowed to integrate the BCRI, complying with real time constraints and behaviors. Experimental results shows that the ability to generate BCI commands is due to a lower user frustration, which is obtained when the perceptual task matches the dynamic attributes exhibited by a dynamic robot. In contrast, when the robot behaves with kinematic attributes only, high frustration impedes the generation of BCI commands.

Bimodal BCI Using Simultaneously NIRS and EEG

Although noninvasive brain-computer interfaces (BCI) based on electroencephalographic (EEG) signals have been studied increasingly over the recent decades, their performance is still limited in two important aspects. First, the difficulty of performing a reliable detection of BCI commands increases when EEG epoch length decreases, which makes high information transfer rates difficult to achieve. Second, the BCI system often misclassifies the EEG signals as commands, although the subject is not performing any task. In order to circumvent these limitations, the hemodynamic fluctuations in the brain during stimulation with steady-state visual evoked potentials (SSVEP) were measured using near-infrared spectroscopy (NIRS) simultaneously with EEG. BCI commands were estimated based on responses to a flickering checkerboard (ON-period). Furthermore, an "idle" command was generated from the signal recorded by the NIRS system when the checkerboard was not flickering (OFF-period). The joint use of EEG and NIRS was shown to improve the SSVEP classification. For 13 subjects, the relative improvement in error rates obtained by using the NIRS signal, for nine classes including the "idle" mode, ranged from 85% to 53 %, when the epoch length increase from 3 to 12s. These results were obtained from only one EEG and one NIRS channel. The proposed bimodal NIRS-EEG approach, including detection of the idle mode, may make current BCI systems faster and more reliable.

Classification of brain hemodynamic signals arising from visual action observation tasks for brain–computer interfaces: A functional near-infrared spectroscopy study

A brain–computer interface (BCI) is a way of translating an individuals’ thoughts to control a computer or an external mechanical device. Studying brain activities in a reproducible manner, this study explores the possibility of using real-time functional-near infrared spectroscopy (fNIRS) to detect brain hemodynamic features for BCI commands. Sixteen channel brain activities associated with two distinct mental tasks were measured from seven healthy subjects. The tasks represented neural activities arising from a visual observation of a motor action related to hand movements of the subjects. Sensitive signatures of task relevant neural activities were further extracted from hemodynamic signals in the prefrontal cortex of the brain, and subsequently were translated into pre-determined computer commands using a set of algorithms. The decoded commands allowed volunteer subjects to control an external device in real-time through their mental intentions. The obtained results demonstrate the potential of the current study as an alternative fNIRS-BCI paradigm.

BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

We present a brain-computer interface (BCI) version of the famous “Connect Four”. Target selection is based on brain event-related responses measured with nine EEG sensors. Two players compete against each other using their brain activity only. Importantly, we turned the general difficulty of producing a reliable BCI command into an advantage, by extending the game play and rules, in a way that adds fun to the game and might well prove to trigger up motivation in future studies. The principle of this new BCI is directly inspired from our own implementation of the classical P300 Speller (Maby et al. 2010, Perrin et al. 2011). We here establish a proof of principle that the same electrophysiological markers can be used to design an efficient two-player game. Experimental evaluation on two competing healthy subjects yielded an average accuracy of 82%, which is in line with our previous results on many participants and demonstrates that the BCI “Connect Four” can effectively be controlled. Interestingly, the duration of the game is not significantly affected by the usual slowness of BCI commands. This suggests that this kind of BCI games could be of interest to healthy players as well as to disabled people who cannot play with classical games.