UMA-BCI Speller: An easily configurable P300 speller tool for end users

Considerations and discussions on the clear definition and definite scope of brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.

Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain-Computer Interface Speller under Rapid Serial Visual Presentation

Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces-particularly those tinted red-have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems.

Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain-Computer Interface

A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain-computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller.

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

Brain-computer interfaces can provide a new communication channel and control functions to people with restricted movements. Recent studies have indicated the effectiveness of brain-computer interface (BCI) applications. Various types of applications have been introduced so far in this field, but the number of those available to the public is still insufficient. Thus, there is a need to expand the usability and accessibility of BCI applications. In this study, we introduce a BCI application for users to experience a virtual world tour. This software was built on three open-source environments and is publicly available through the GitHub repository. For a usability test, 10 healthy subjects participated in an electroencephalography (EEG) experiment and evaluated the system through a questionnaire. As a result, all the participants successfully played the BCI application with 96.6% accuracy with 20 blinks from two sessions and gave opinions on its usability (e.g., controllability, completeness, comfort, and enjoyment) through the questionnaire. We believe that this open-source BCI world tour system can be used in both research and entertainment settings and hopefully contribute to open science in the BCI field.

Brain-Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Brain-computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.

Different effects of using pictures as stimuli in a P300 brain-computer interface under rapid serial visual presentation or row-column paradigm

Previous proposals for controlling a P300-based BCI speller have shown an improvement using alternative images instead of letters as target stimuli under a row-column paradigm (RCP). However, the RCP is not suitable for those patients with a lack of gaze control. To solve that, the rapid serial visual presentation (RSVP) paradigm has been proposed in previous studies. The aim of the present work is to assess if a set of alternative pictures that improved performance in RCP could also improve performance in RSVP. Sixteen participants controlled four conditions in calibration and online tasks: letters in RCP, pictures in RCP, letters in RSVP and pictures in RSVP. The effect given by pictures was greater under RCP than under RSVP, both for performance and event-related potential analyses. Indeed, pictures did not show any improvement under RSVP in comparison to letters. In addition, the condition with pictures under RCP was declared the favourite by most users (68.75%), while the condition with pictures under RSVP was not chosen as favourite by any participant. Therefore, this work shows that the improvement related to the use of pictures as alternative flashing stimuli under RCP may not be transferred to RSVP. Graphical abstract.

Performance Analysis With Different Types of Visual Stimuli in a BCI-Based Speller Under an RSVP Paradigm

Brain-Computer Interface (BCI) systems enable an alternative communication channel for severely-motor disabled patients to interact with their environment using no muscular movements. In recent years, the importance of research into non-gaze dependent brain-computer interface paradigms has been increasing, in contrast to the most frequently studied BCI-based speller paradigm (i.e., row-column presentation, RCP). Several visual modifications that have already been validated under the RCP paradigm for communication purposes have not been validated under the most extended non-gaze dependent rapid serial visual presentation (RSVP) paradigm. Thus, in the present study, three different sets of stimuli were assessed under RSVP, with the following communication features: white letters (WL), famous faces (FF), neutral pictures (NP). Eleven healthy subjects participated in this experiment, in which the subjects had to go through a calibration phase, an online phase and, finally, a subjective questionnaire completion phase. The results showed that the FF and NP stimuli promoted better performance in the calibration and online phases, being slightly better in the FF paradigm. Regarding the subjective questionnaires, again both FF and NP were preferred by the participants in contrast to the WL stimuli, but this time the NP stimuli scored slightly higher. These findings suggest that the use of FF and NP for RSVP-based spellers could be beneficial to increase information transfer rate in comparison to the most frequently used letter-based stimuli and could represent a promising communication system for individuals with altered ocular-motor function.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

Background and Objective: Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still not enough for online use. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component. Moreover, we have developed an upper-limb assist robot system with the method for verification and hope to really help paralyzed people.

Methods: We chose P300, which is highly available and easily accepted to obtain the user's intention. Preprocessing and spatial enhancement were firstly implemented on raw EEG data. Then, three approaches– linear discriminant analysis, support vector machine, and multilayer perceptron –were compared in detail to accomplish an efficient P300 detector, whose output was employed as a command to control the assist robot.

Results: The method we proposed achieved an accuracy of 94.43% in the offline test with the data from eight participants. It showed sufficient reliability and robustness with an accuracy of 80.83% and an information transfer rate of 15.42 in the online test. Furthermore, the extended test showed remarkable generalizability of this method that can be used in more complex application scenarios.

Conclusion: From the results, we can see that the proposed method has great potential for helping paralyzed people easily control an assist robot to do numbers of things.

Effects of Spatial Stimulus Overlap in a Visual P300-based Brain-computer Interface

The rapid serial visual presentation (RSVP) paradigm seems to be one of the most appropriate for patients using P300-based brain-computer interface (BCI) applications, since non-ocular movements are required. However, according to previous works, the use of different locations for each stimulus may improve performance. Thus, the aim of the present work is to explore how spatial overlap between stimuli influences performance in controlling a visual P300-based BCI. Nineteen participants were tested using four levels of overlap between two stimuli: 100%, 66.7%, 33.3% and 0%. Significant differences in accuracy were found between the 0% overlapped condition and all the other conditions, and between 33.3% and higher overlap (66.7% and 100%). These results can be explained due to a modulation in the non-target stimulus amplitude signal caused by the overlapping factor. In short, the stimulus overlap provokes a modulation in performance using a P300-based BCI; this should be considered in future BCI proposals in which an optimal surface exploitation is convenient and potential users have only residual ocular movement.

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

BACKGROUND AND OBJECTIVE

Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).

METHODS

Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.

RESULTS

The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.

CONCLUSION

These findings contribute to the development of more safe and practical BCI.

Evaluation of emotional and neutral pictures as flashing stimuli using a P300 brain-computer interface speller

OBJECTIVE

Previous works have reported that complex emotional and visual stimuli can increase the amplitude of the P300 brain potential. Thus, the aim of the present work is to assess these kinds of images in a P300 brain-computer interface (BCI) speller as flashing stimuli.

APPROACH

Twenty-three volunteers controlled four spellers with different sets of flashing stimuli: flashing letters, neutral pictures (NP), emotional pleasant pictures (EPP) and emotional unpleasant pictures (EUP).

MAIN RESULTS

The sets of pictures showed a higher performance than the letters in accuracy and information transfer rate. These results were supported by the analysis of the P300 signal, where the picture sets offered the greatest amplitudes. The NP and EPP sets were the best evaluated in the subjective questionnaire.

SIGNIFICANCE

In short, despite the fact that the effect of emotional stimuli could not be observed in the performance metrics, picture sets have offered a high performance and should be considered in future proposals for visual P300-based BCI applications.